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+The State of Human-centered NLP Technology for Fact-checking
+Anubrata Das∗, Houjiang Liu, Venelin Kovatchev and Matthew Lease
+aSchool of Information, The University of Texas at Austin, Austin, TX, USA
+A R T I C L E I N F O
+Keywords:
+Natural Language Processing
+Misinformation
+Disinformation
+Explainability
+Human-AI Teaming
+A B S T R A C T
+Misinformation threatens modern society by promoting distrust in science, changing narratives in
+public health, heightening social polarization, and disrupting democratic elections and financial
+markets, among a myriad of other societal harms. To address this, a growing cadre of professional
+fact-checkers and journalists provide high-quality investigations into purported facts. However,
+these largely manual efforts have struggled to match the enormous scale of the problem. In
+response, a growing body of Natural Language Processing (NLP) technologies have been
+proposed for more scalable fact-checking. Despite tremendous growth in such research, however,
+practical adoption of NLP technologies for fact-checking still remains in its infancy today.
+In this work, we review the capabilities and limitations of the current NLP technologies for
+fact-checking. Our particular focus is to further chart the design space for how these technologies
+can be harnessed and refined in order to better meet the needs of human fact-checkers. To do
+so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction,
+modeling, and human-centered strategies, such as explainable models and human-in-the-loop
+approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist
+human fact-checkers. We recommend that future research include collaboration with fact-checker
+stakeholders early on in NLP research, as well as incorporation of human-centered design
+practices in model development, in order to further guide technology development for human
+use and practical adoption. Finally, we advocate for more research on benchmark development
+supporting extrinsic evaluation of human-centered fact-checking technologies.
+1. Introduction
+Misinformation and related issues (disinformation, deceptive news, clickbait, rumours, and information credibility)
+increasingly threaten society. While concerns of misinformation existed since the early days of written text (Marcus,
+1992), with recent development of social media, the entry barrier for creating and spreading content has never been
+lower. Moreover, polarization online drives the spread of misinformation that in turn increases polarization (Cinelli,
+Pelicon, Mozetič, Quattrociocchi, Novak and Zollo, 2021a,b; Vicario, Quattrociocchi, Scala and Zollo, 2019). Braking
+such a vicious cycle would require addressing the problem of misinformation at its root.
+Fields such as journalism (Graves, 2018b; Graves and Amazeen, 2019; Neely-Sardon and Tignor, 2018) and
+archival studies (LeBeau, 2017) have extensively studied misinformation, and recent years have seen a significant
+growth in fact-checking initiatives to address this problem. Various organizations now focus on fact-checks (e.g.,
+PolitiFact, Snopes, FactCheck, First Draft, and Full Fact), and organizations such as the International Fact-Checking
+Network (IFCN)1 train and provide resources for independent fact-checkers and journalists to further scale expert
+fact-checking.
+While professional fact-checkers and journalists provide high-quality investigations of purported facts to inform
+the public, human effort struggles to match the global Internet scale of the problem. To address this, a growing
+body of research has investigated Natural Language Processing (NLP) to fully or partially automate fact-checking
+(Guo, Schlichtkrull and Vlachos, 2022; Nakov, Corney, Hasanain, Alam, Elsayed, Barrón-Cedeño, Papotti, Shaar and
+Da San Martino, 2021a; Zhou and Zafarani, 2020; Zeng, Abumansour and Zubiaga, 2021; Graves, 2018a). However,
+even state-of-the-art NLP technologies still cannot match human capabilities in many areas and remain insufficient
+to automate fact-checking in practice. Experts argue (Arnold, 2020; Nakov et al., 2021a) that fact-checking is a
+complex process and requires subjective judgement and expertise. While current NLP systems are increasingly better
+at addressing simple fact-checking tasks, identifying false claims that are contextual and beyond simple declarative
+∗Corresponding author
+ORCID(s): 0000-0002-5412-6149 (A. Das); 0000-0003-0983-6202 (H. Liu); 0000-0003-1259-1541 (V. Kovatchev);
+0000-0002-0056-2834 (M. Lease)
+1https://www.politifact.com/,
+https://www.snopes.com/,
+https://www.factcheck.org/,
+https://firstdraftnews.
+org/, https://fullfact.org/, and https://www.poynter.org/ifcn/, respectively.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 1 of 30
+arXiv:2301.03056v1  [cs.CL]  8 Jan 2023
+
+The State of Human-centered NLP Technology for Fact-checking
+statements remains beyond the reach for fully automated systems (Chen, Sriram, Choi and Durrett, 2022; Fan, Piktus,
+Petroni, Wenzek, Saeidi, Vlachos, Bordes and Riedel, 2020). For example, claims buried in conversational systems,
+comment threads in social media community, and claims in multimedia contents are particularly challenging for
+automated systems. Additionally, most fact-checking practitioners desire NLP tools that are integrated into the existing
+fact-checking workflow and reduce latency (Nakov et al., 2021a; Graves, 2018b; Alam, Shaar, Dalvi, Sajjad, Nikolov,
+Mubarak, Da San Martino, Abdelali, Durrani, Darwish, Al-Homaid, Zaghouani, Caselli, Danoe, Stolk, Bruntink and
+Nakov, 2021b).
+In this literature review, we provide the reader with a comprehensive and holistic overview of the current state-
+of-the-art challenges and opportunities to more effectively leverage NLP technology in fact-checking. Our objectives
+in this work are twofold. First, we cover all aspects of the NLP pipeline for fact checking:
+task formulation, dataset
+construction, and modeling approaches. Second, we emphasize the human-centered approaches that seek to augment
+and accelerate human fact-checking, rather than supplant it. In contrast, prior literature reviews (Zeng et al., 2021;
+Guo et al., 2022; Oshikawa, Qian and Wang, 2020) either provide an overview of the existing approaches or capture
+the details of only a specific part of the fact-checking pipeline (Kotonya and Toni, 2020a; Hardalov, Arora, Nakov and
+Augenstein, 2021; Hanselowski, AvineshP.V., Schiller, Caspelherr, Chaudhuri, Meyer and Gurevych, 2018; Demartini,
+Mizzaro and Spina, 2020).
+Furthermore, we argue that it is important to extend the review of NLP technologies for fact-checking from
+modeling development to the area of Human-Computer Interaction (HCI) because technology design should reflect
+user needs so that its development can be better integrated in the real-world use context (Graves, 2018a; Lease, 2020;
+Kovatchev, Smith, Lee, Traynor, Aguilera and Devine, 2020; Nakov et al., 2021a; Micallef, Armacost, Memon and
+Patil, 2022; Juneja and Mitra, 2022). Specifically, we point the reader towards Section 7 where we propose concrete
+directions for future work.
+Current challenges are largely due to the relatively early stage of development of the automated fact-checking tech-
+nology. Specifically, current studies tend to adopt an intrinsic evaluation of components of the fact-checking pipeline
+rather than an end-to-end extrinsic evaluation of the entire fact-checking task. Moreover, component-wise accuracies
+may remain below the threshold required for practical adoption. Furthermore, while the research community’s focus
+on prediction accuracy has yielded laudable improvements, human factors (e.g., usability, intelligibility, trust) have
+garnered far less attention or progress yet are crucial for practical adoption.
+Such limitations have implications for future research. First, practical use of NLP technologies for fact-checking
+is likely to come from hybrid, human-in-the-loop approaches rather than full automation. Second, as the technology
+matures, end-to-end evaluation becomes increasingly important to ensure practical solutions are being developed to
+solve the real-world use-case. To this end, new benchmarks that facilitate the extrinsic evaluation of automated fact-
+checking applications in practical settings may help drive progress on solutions that can be adopted for use in the wild.
+Finally, to craft effective human-in-the-loop systems, more cross-cutting NLP and HCI integration could strengthen
+design of fact-checking tools, so that they are accurate, scalable, and usable in practice. Toward this end, it may
+be fruitful to collaborate more with stakeholders early on in NLP research and incorporate human-centered design
+practices in developing models.
+We have written this article with different audiences in mind. For researchers and fact-checkers who are new to
+automated fact-checking, this article provides a comprehensive overview of the problem. We discuss the challenges,
+the state-of-the-art capabilities, and the opportunities in the field, and we emphasize how machine learning and natural
+language processing can be used to combat disinformation. We recommend researchers new to this topic read the
+article in its entirety, following the logical structure of sections. Other readers who have more experience in the
+field may already be familiar with some of the concepts that we discuss. For them, this paper offers a novel human-
+centered perspective of automated fact checking and a discussion on how that perspective can affect system design,
+implementation, and evaluation. To facilitate the use of the paper by more experienced readers, we provide a quick
+overview of the content covered in each section.
+• Section 2 introduces the automated fact-checking pipeline. We provide an overview of the process for human
+fact-checkers and for automated solutions.
+• Section 3 discusses the task formulation: the goals and formal definitions of different sub-tasks in fact checking.
+• Section 4 describes the process of dataset construction, presents the most popular corpora for automated fact
+checking, and outlines some limitations of the data.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 2 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+Figure 1: Fact-checking pipeline
+• Section 5 reviews approaches for automating fact checking. We discuss general NLP capabilities (Section 5.1),
+explainable approaches (Section 5.2), and human-in-the-loop (Section 5.3) approaches for fact-checking.
+• Section 6 surveys existing tools that apply NLP for fact-checking in a practical, real-world context. We argue
+that the human-centered perspective is necessary for the practical adoption of automated solutions.
+• Section 7 provides future research directions in the context of human-centered fact checking. We discuss the work
+division between human and AI for mixed-initiative fact-checking in Section 7.1. In Section 7.2 we propose a
+novel concept for measuring trust and a novel human-centered evaluation of NLP to assist fact-checkers.
+• We conclude our literature review with Section 8.
+2. Fact-Checking Pipeline
+The core idea behind automated fact-checking is enabling AI to reason over available information to determine the
+truthfulness of a claim. For successful automation, it is essential first to understand the complex process of journalistic
+fact-checking that involves human expertise along with skilled effort towards gathering evidence and synthesizing
+the evidence. Additional complexity comes from the need to process heterogeneous sources (e.g., information across
+various digital and non-digital sources). Data is also spread across different modalities such as images, videos, tables,
+graphs, among others. Moreover, there is a lack of tools that support effective and efficient fact-checking workflows
+(Graves, 2018a; Nakov et al., 2021a; Arnold, 2020).
+Graves (2017) breaks down the practical fact-checking mechanism for human fact-checkers into multiple steps
+such as a) identifying the claims to check, b) tracing false claims, c) consulting experts, and d) sharing the resulting
+fact-check. A growing body of AI literature — specifically in NLP — focuses on automating the fact-checking process.
+We synthesize several related surveys (Guo et al., 2022; Nakov et al., 2021a; Graves, 2018a; Zeng et al., 2021;
+Micallef et al., 2022) and distinguish four typical stages that constitute the automated fact-checking technology pipeline
+(illustrated in Figure 1). Note that the pipeline we describe below closely follows the structure of Guo et al. (2022),
+though the broader literature is also incorporated within these four stages:
+• Claim Detection, Checkworthiness, and Prioritization: Claim detection involves monitoring news and/or
+online information for potentially false content to fact-check. One must identify claims that are potentially
+falsifiable (e.g., purported facts rather subjective opinions) (Guo et al., 2022; Zeng et al., 2021). Moreover,
+because it is impractical to fact-check everything online given limited fact-checking resources (human or
+automated), fact checkers must prioritize what to fact-check (Arnold, 2020). NLP researchers have sought to
+inform such prioritization by automatically predicting the "checkworthiness" of claims (Nakov et al., 2021a).
+Additionally, to avoid repeated work, fact-checkers may consult existing fact-checking databases before judging
+the veracity of a new claim (claim matching (Zeng et al., 2021)). We see claim matching as a part of prioritizing
+claims, as fact-checkers would prioritize against checking such claims.
+• Evidence Retrieval: Once it is clear which claims to fact-check, the next step is to gather relevant, trustworthy
+evidence for assessing the claim (Guo et al., 2022; Zeng et al., 2021).
+• Veracity Prediction: Given the evidence, it is necessary to assess it to determine the veracity of the claim (Guo
+et al., 2022; Zeng et al., 2021).
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 3 of 30
+
+回田
+β
+R.m
+Claim detection,
+Evidence
+Veracity
+Checkworthiness,
+Explanation
+retrieval
+prediction
+and PrioritizationThe State of Human-centered NLP Technology for Fact-checking
+• Explanation: Finally, for human use, one must explain the fact-checking outcome via human-understandable
+justification for the model’s determination (Graves, 2018a; Kotonya and Toni, 2020a; Guo et al., 2022).
+In the subsequent sections, we discuss each of the tasks above in the context of existing NLP research in automated
+fact-checking. Some other steps (for example, detecting propaganda in text, click-bait detection) are also pertinent to
+fact-checking but do not directly fit into the stages described above. They are briefly discussed in Section 3.5.
+3. Task Formulation for Automated Fact-Checking
+Modern Natural Language Processing is largely-data driven. In this article, we distinguish task formulation
+(conceptual) vs. dataset construction (implementation activity, given the task definition). That said, the availability
+of a suitable dataset or the feasibility of constructing a new dataset can also bear on how tasks are formulated.
+3.1. Claim Detection, Checkworthiness, and Prioritization
+Fact-checkers and news organizations monitor information sources such as social media (Facebook, Twitter, Reddit,
+etc.), political campaigns and speeches, and public addresses from government officials on critical issues (Arnold,
+2020; Nakov et al., 2021a). Additional sources include tip-lines on end-to-end encrypted platforms (such as WhatsApp,
+Telegram, and Signal) (Kazemi, Garimella, Shahi, Gaffney and Hale, 2021b). The volume of information on various
+platforms makes it challenging to efficiently monitor all sources for misinformation. Zeng et al. (2021) define the claim
+detection step as identifying, filtering, and prioritizing claims.
+To identify claims, social media streams are often monitored for rumors (Guo et al., 2022). A rumour can be
+defined as a claim that is unverified and being circulated online (Zubiaga, Aker, Bontcheva, Liakata and Procter, 2018).
+Rumours are characterized by the subjectivity of the language and the reach of the content to the users (Qazvinian,
+Rosengren, Radev and Mei, 2011). Additionally, metadata related to virality, such as the number of shares (or retweets
+and re-posts), likes, or comments are also considered when identifying whether a post is a rumour (Zhang, Cao, Li,
+Sheng, Zhong and Shu, 2021a; Gorrell, Kochkina, Liakata, Aker, Zubiaga, Bontcheva and Derczynski, 2019). However,
+detecting rumours alone is not sufficient to decide whether a claim needs to be fact-checked.
+For each text of interest, the key questions fact-checking systems need to address include:
+1. Is there a claim to check?
+2. Does the claim contain verifiable information?
+3. Is the claim checkworthy?
+4. Has a trusted source already fact-checked the claim?
+Regarding the first criterion — is there a claim — one might ask whether the claim contains a purported fact or
+an opinion (Hassan, Arslan, Li and Tremayne, 2017a). For example, a statement such as “reggae is the most soulful
+genre of music” represents personal preference that is not checkable. In contrast, “<NAME> won a gold medal in the
+Olympics” is checkable by matching <NAME> to the list of all gold medal winners.
+Whether the claim contains verifiable information is more challenging. For example, if a claim can only be verified
+by private knowledge or personal experience that is not broadly accessible, then it cannot be checked (Konstantinovskiy,
+Price, Babakar and Zubiaga, 2021). For example, if someone claims to have eaten a certain food yesterday, it is probably
+impossible to verify beyond their personal testimony.
+As this example suggests, the question of whether the claim contains verifiable information depends in large part
+on what evidence is available for verification. This, in turn, may not be clear until after evidence retrieval is performed.
+In practice fact-checkers may perform some preliminary research, but mostly try to gauge checkworthiness only based
+on the claim itself.
+In addition, this consideration is only one of many that factors into deciding whether to check a claim. Even a claim
+that may appear to be unverifiable may still be of such great public interest that it is worth conducting the fact-check.
+Moreover, even if the fact-check is conducted and ultimately indeterminate (i.e., evidence does not exist either to verify
+or refute the claim), simply showing that a claim’s veracity cannot be determined may still be a valuable outcome.
+A claim is deemed checkworthy if a claim is of significant public interest or has the potential to cause harm (Nakov,
+Da San Martino, Elsayed, Barrón-Cedeño, Míguez, Shaar, Alam, Haouari, Hasanain, Babulkov et al., 2021b; Hassan,
+Li and Tremayne, 2015). For example, a claim related to the effect of a vaccine on the COVID-19 infection rate is more
+relevant to the public interest and hence more checkworthy than a claim about some philosopher’s favorite food.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 4 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+Claims, like memes, often appear several times and/or across multiple platforms (in the same form or with
+slight modification) (Leskovec, Backstrom and Kleinberg, 2009; Nakov et al., 2021a; Arnold, 2020). Fact-checking
+organizations maintain a growing database claims which have already been fact-checked. Thus, detected claims are
+compared against databases of already fact-checked claims by trusted organizations (Shaar, Martino, Babulkov and
+Nakov, 2020; Shaar, Alam, Da San Martino and Nakov, 2021a). Comparing new claims against such databases helps
+to avoid duplicating work on previously fact-checked claims. This step is also known as claim matching (Zeng et al.,
+2021).
+Reports from practitioners argue that if a claim is not checked within the first few hours, a late fact-check
+does not have much impact on changing the ongoing misinformation narrative (Nakov et al., 2021a; Arnold, 2020).
+Moreover, limited resources for fact-checking make it crucial for organizations to prioritize the claims to be checked
+(Borel, 2016). Claims can be prioritized based on their checkworthiness (Nakov, Da San Martino, Barrón-Cedeño,
+Zaghouani, Míguez, Alam, Caselli, Kutlu, Strub and Mandl, 2022; Nakov et al., 2021b). Nakov et al. (2022) note that
+checkworthiness is determined based on factors such as
+1. How urgently a claim needs to be checked?
+2. How much harm can a claim cause (Alam et al., 2021b; Alam, Dalvi, Shaar, Durrani, Mubarak, Nikolov,
+Da San Martino, Abdelali, Sajjad, Darwish and Nakov, 2021a; Shaar, Hasanain, Hamdan, Ali, Haouari, Nikolov,
+Kutlu, Kartal, Alam, Da San Martino, Barrón-Cedeño, Míguez, Beltrán, Elsayed and Nakov, 2021b)?
+3. Would the claim require attention from policy makers for addressing the underlying issue?
+Note that estimating harms is quite challenging, especially without first having a thorough understanding and measures
+of harm caused by misinformationNeumann, De-Arteaga and Fazelpour (2022).
+The spread of a claim on social media provides another potential signal for identifying public interest (Arnold,
+2020). In the spirit of doing “the greatest good for the greatest number”, viral claims might be prioritized highly because
+any false information in them has the potential to negatively impact a large number of people. On the other hand, since
+fairness considerations motivate equal protections for all people, we cannot serve only the majority at the expense of
+minority groups (Ekstrand, Das, Burke, Diaz et al., 2022; Neumann et al., 2022). Moreover, such minority groups may
+be more vulnerable, motivating greater protections, and may be disproportionately impacted by mis/disinformation
+(Guo et al., 2022). See Section 3.6 for additional discussion.
+3.2. Evidence Retrieval
+Some sub-tasks in automated fact-checking can be performed without the presence of explicit evidence. For
+example, the linguistic properties of the text can be used to determine whether it is machine-generated (Wang, 2017;
+Rashkin, Choi, Jang, Volkova and Choi, 2017). However, assessing assessing claim veracity without evidence is clearly
+more challenging (Schuster, Schuster, Shah and Barzilay, 2020).
+Provenance of a claim can also signal information quality; known unreliable source or distribution channels are
+often repeat offenders in spreading false information2. Such analysis of provenance can be further complicated when
+content is systematically propagated by multiple sources (twitter misinformation bots) (Jones, 2019).
+It is typically assumed that fact-checking requires gathering of reliable and trustworthy evidence that provides
+information to reason about the claim (Graves, 2018a; Li, Gao, Meng, Li, Su, Zhao, Fan and Han, 2016). In some
+cases, multiple aspects of a claim needs to be checked. A fact-checker would then decompose such a claim into distinct
+questions and gather relevant evidence for the question (Borel, 2016; Chen et al., 2022). From an information retrieval
+(IR) perspective, we can conceptualize each of those questions as an “information need” for which the fact-checker must
+formulate one or more queries to a search engine (Bendersky, Metzler and Croft, 2012) in order to retrieve necessary
+evidence.
+Evidence can be found across many modalities, including text, tables, knowledge graphs, images, and videos.
+Various metadata can also provide evidence and are sometimes required to assess the claim. Examples include context
+needed to disambiguate claim terms, or background of the individual or organization from whom the claim originated.
+Retrieving relevant evidence also depends on the following questions (Singh, Das, Li and Lease, 2021):
+1. Is there sufficient evidence available related to a claim?
+2. Is it accessible or available in the public domain?
+3. Is it in a format that can be read and processed?
+2https://disinformationindex.org/
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 5 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+As noted earlier in Section 3.1, the preceding claim detection task involves assessing whether a claim contains verifiable
+information; this depends in part on what evidence exists to be retrieved, which is not actually known until evidence
+retrieval is performed. Having now reached this evidence retrieval step, we indeed discover whether sufficient evidence
+exists to support or refute the claim.
+Additionally, evidence should be trustworthy, reputable (Nguyen, Kharosekar, Lease and Wallace, 2018b; Lease,
+2018), and unbiased (Chen, Khashabi, Yin, Callison-Burch and Roth, 2019a).
+Once evidence is retrieved, stance detection assesses the degree to which the evidence supports or refutes the
+claim (Nguyen, Kharosekar, Krishnan, Krishnan, Tate, Wallace and Lease, 2018a; Ferreira and Vlachos, 2016; Popat,
+Mukherjee, Yates and Weikum, 2018). Stance detection is typically formulated as a classification task (or ordinal
+regression) over each piece of retrieved evidence. Note that some works formulate stance detection as an independent
+task (Hanselowski et al., 2018; Hardalov et al., 2021).
+3.3. Veracity Prediction
+Given a claim and gathered evidence, veracity prediction involves reasoning over the collected evidence and the
+claim. Veracity prediction can be formulated as a binary classification task (i.e., true vs. false) (Popat et al., 2018;
+Nakashole and Mitchell, 2014; Potthast, KIESELJ et al., 2018), or as a fine-grained, multi-class task following the
+journalistic fact-checking practices (Augenstein, Lioma, Wang, Lima, Hansen, Hansen and Simonsen, 2019; Shu,
+Mahudeswaran, Wang, Lee and Liu, 2020; Wang, 2017). In some cases, there may not be enough information available
+to determine the veracity of a claim (Thorne, Vlachos, Christodoulopoulos and Mittal, 2018).
+Note that fact-checking is potentially a recursive process because retrieved evidence may itself need to be fact-
+checked before it can be trusted and acted upon (Graves, 2018a). This is also consistent with broader educational
+practices in information literacy3 in which readers are similarly encouraged to evaluate the quality of information
+they consume. Such assessment of information reliability can naturally integrate with the veracity prediction task in
+factoring in the reliability of the evidence along with its stance (Nguyen et al., 2018b; Guo et al., 2022).
+3.4. Explaining Veracity Prediction
+While a social media platform might use automated veracity predictions in deciding whether to automatically block
+or demote content, the use of fact-checking technology often involves a human-in-the-loop, whether it is a platform
+moderator, a journalist, or an end-user. When we consider such human-centered use of fact-checking technologies,
+providing an automated veracity prediction without justifying the answer can cause a system to be ignored or distrusted,
+or even reinforce mistaken human beliefs in false claims (the “backfire effect” (Lewandowsky, Ecker, Seifert, Schwarz
+and Cook, 2012)). Explanations and justifications are especially important given the noticeable drop in performance of
+state-of-the-art NLP systems when facing adversarial examples (Kovatchev, Chatterjee, Govindarajan, Chen, Choi,
+Chronis, Das, Erk, Lease, Li et al., 2022). Consequently, automated fact-checking systems intended for human-
+consumption should seek to explain their veracity predictions in a similar manner to that of existing journalistic
+fact-checking practices (Uscinski, 2015). A brief point to make is that much of the explanation research has focused
+on explanations for researchers and engineers engaged in system development (types of explanations, methods of
+generating them, and evaluation regimens). In contrast,we emphasize here explanations for system users.
+Various types of explanations can be provided, such as through
+1. evidence attribution
+2. explaining the decision-making process for a fact-check
+3. summarizing the evidence
+4. case-based explanations
+Evidence attribution is the process of identifying evidence or a specific aspect of the evidence (such as paragraphs,
+sentences, or even tokens of interest) (Thorne et al., 2018; Popat et al., 2018; Shu, Cui, Wang, Lee and Liu, 2019; Lu
+and Li, 2020). Furthermore, the relative importance of the evidence can also justify the fact-checking outcome (Nguyen
+et al., 2018a). Alternatively, a set of rules or interactions to break down parts of the decision-making process can also
+serve as an explanation (Gad-Elrab, Stepanova, Urbani and Weikum, 2019; Nguyen et al., 2018b). Such formulation
+focuses more on how the evidence is processed to arrive at a decision. Explaining the veracity can also be formulated
+as a summarization problem over the gathered evidence to explain a fact-check (Atanasova, Simonsen, Lioma and
+Augenstein, 2020a; Kotonya and Toni, 2020b). Finally, case-based explanations can provide the user with similar,
+human-labeled instances (Das, Gupta, Kovatchev, Lease and Li, 2022).
+3https://en.wikipedia.org/wiki/Information_literacy
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 6 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+3.5. Related Tasks
+In addition to tasks that are considered central to the automated fact-checking pipeline, some additional tasks bear
+mentioning as related and complementary to the fact-checking enterprise. Examples of such tasks include propaganda
+detection (Da San Martino, Cresci, Barrón-Cedeño, Yu, Pietro and Nakov, 2020), clickbait detection (Potthast, Köpsel,
+Stein and Hagen, 2016), and argument mining (Lawrence and Reed, 2020). Furthermore, some tasks can be formulated
+independent of the fact-checking pipeline and utilized later to improve individual fact-checking sub-tasks. For example,
+predicting the virality of social media content (Jain, Garg and Jain, 2021) can help improve claim detection and claim
+checkworthiness. Similarly, network analysis on fake news propagation (Shao, Ciampaglia, Flammini and Menczer,
+2016) can help in analyzing provenance.
+With an eye toward building more human-centered AI approaches, there are also some tasks that could be applied
+to help automate parts of the fact-checking process. For example, claim detection might be improved via an URL
+recommendation engine for content that might need fact-checking (Vo and Lee, 2018). Additionally, fact-checkers
+could benefit from a predicted score for claim difficulty (Singh et al., 2021). In terms of evidence retrieval and
+veracity prediction, one might generate fact-checking briefs to aid inexpert fact-checkers (Fan et al., 2020). Instead
+of summarizing the evidence in general (Section 3.4), one might instead summarize with the specific goal of decision
+support (Hsu and Tan, 2021).
+3.6. Key Challenges
+Most work in automated fact-checking has been done on veracity prediction, and to a lesser extent, on explanation
+generation. Recently, we have seen more attention directed towards claim detection and checkworthiness. In contrast,
+work on evidence retrieval remains less developed.
+Claim Detection Guo et al. (2022) points out several sources of biases in the claim check-worthiness task. Claims
+could be of variable interest to different social groups. Additionally, claims that might cause more harm to marginalized
+groups compared to the general population may not get enough attention. Ideally, models identifying check-worthiness
+need to overcome any possible disparate impact.
+Similar concerns appear in the report by Full Fact (Arnold, 2020). One of the criteria for selecting a claim for fact-
+checking across several organizations is “Could the claim threaten democratic processes or minority groups?” However,
+such criterion may be at odds with the concerns of virality. Fact-checking organizations often monitor virality metrics
+to decide which claims to fact-check (Arnold, 2020; Nakov et al., 2021a). Nevertheless, if a false claim is targeted
+towards an ethnic minority, such claims may not cross the virality thresholds.
+Prioritizing which claims to fact-check requires attention to various demographic traits: content creators, readers,
+and subject matter. Claim check-worthiness dataset design can thus benefit from consideration of demographics.
+Evidence Retrieval Evidence retrieval has been largely neglected in the automated fact-checking NLP literature. It
+is often assumed that evidence is already available, or, coarse-grained evidence is gathered from putting the claims into
+a search engine (Popat et al., 2018; Nguyen et al., 2018a). However, Hasanain and Elsayed (2022) show in their study
+that search engines optimized for relevance seldom retrieve evidence most useful for veracity prediction. Although
+retrieving credible information has been studied thoroughly in IR (Clarke, Rizvi, Smucker, Maistro and Zuccon,
+2020a), more work is needed that is focused on retrieving evidence for veracity assessment (Lease, 2018; Clarke,
+Rizvi, Smucker, Maistro and Zuccon, 2020b).
+Veracity Prediction and Explanation A critical challenge for automated systems is to reason over multiple sources
+of evidence while also taking source reputation into account. Additionally, explaining a complex reasoning process is
+a non-trivial task. The notion of model explanations itself is polysemous and evolving in general, not to mention in the
+context of fact checking. As explainable NLP develops, automated fact-checking tasks also need to evolve and provide
+explanations that are accessible to human stakeholders yet faithful to the underlying model. For example, case-based
+explanations are mostly unexplored in automated fact-checking, although working systems have been proposed for
+propaganda detection (Das et al., 2022).
+In many NLP tasks, such as machine translation or natural language inference, the goal is to build fully-automated,
+end-to-end solutions. However, in the context of fact-checking, state-of-the-art limitations suggest the need for humans-
+in-the-loop for the forseable future. Given this, automated tooling to support human fact-checkers is crucial. However,
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+understanding the fact-checker needs and incorporating those needs in the task formulation has been largely absent
+from the automated fact-checking literature, with a few notable exceptions (Nakov et al., 2021a; Demartini et al., 2020).
+Future research could benefit from greater involvement of fact-checkers in the NLP research process and shifting goals
+from complete automation toward human support.
+4. Dataset Construction
+Corresponding to task formulation (Section 3), our presentation of fact-checking datasets is also organized around
+claims, evidence, veracity prediction, and explanation. Note that not all datasets have all of these components.
+4.1. Claim detection and claim check-worthiness
+Claim detection datasets typically contain claims and their sources (documents, social media streams, transcripts
+from political speeches) (Guo et al., 2022). One form of claim detection is identifying rumours on social media, where
+datasets are primarily constructed with text from Twitter (Zubiaga et al., 2018; Qazvinian et al., 2011) and Reddit
+(Gorrell et al., 2019; Lillie, Middelboe and Derczynski, 2019). Some works provide the claims in the context they
+appeared on social media (Zhang et al., 2021a; Ma, Gao, Mitra, Kwon, Jansen, Wong and Cha, 2016). However,
+several studies note that most claim detection datasets do not contain enough context. As the discussion of metadata
+in Section 3 suggests, broader context might include: social media reach, virality metrics, the origin of a claim, and
+relevant user data (i.e., who posted a claim, how influential they are online, etc.) (Arnold, 2020; Nakov et al., 2021a).
+Claim check-worthiness datasets (Nakov et al., 2021b; Shaar et al., 2021b; Barrón-Cedeño, Elsayed, Nakov,
+Da San Martino, Hasanain, Suwaileh, Haouari, Babulkov, Hamdan, Nikolov et al., 2020; Atanasova, Barrón-Cedeño,
+Elsayed, Suwaileh, Zaghouani, Kyuchukov, Da San Martino and Nakov, 2018; Konstantinovskiy et al., 2021; Hassan
+et al., 2015) filter claims from a source (similar to claim detection, sources include social media feeds and political
+debate transcripts, among others) by annotating claims based on the checkworthiness criteria (mentioned in the section
+3.1). Each claim is given a checkworthiness score to obtain a ranked list. Note that claim detection and checkworthiness
+datasets may be expert annotated (Hassan et al., 2015) or crowd annotated (Nakov et al., 2021b; Shaar et al., 2021b;
+Barrón-Cedeño et al., 2020; Atanasova et al., 2018; Konstantinovskiy et al., 2021)4.
+The datasets discussed above do not capture multi-modal datasets, and few do. One such dataset is r/Fakeddit
+(Nakamura, Levy and Wang, 2020). This dataset contains images and associated text content from Reddit as claims.
+Misinformation can also spread through multi-modal memes, and tasks such as Facebook (now Meta) Hateful Memes
+Challenge (Kiela, Firooz, Mohan, Goswami, Singh, Ringshia and Testuggine, 2020) for hate speech suggest what might
+be similarly done for misinformation detection.
+4.2. Evidence
+Early datasets in fact-checking provide metadata with claims as the only form of evidence. Such metadata include
+social media post properties, user information, publication date, source information (Wang, 2017; Potthast et al., 2018).
+As discussed earlier in Section 3.2, such metadata does not contain the world knowledge necessary to reason about a
+complex claim. To address the above limitations, recent datasets consider external evidence (Guo et al., 2022).
+Evidence is collected differently depending upon the problem setup. For artificial claims, evidence is often retrieved
+from a single source such as Wikipedia articles (Thorne et al., 2018; Jiang, Bordia, Zhong, Dognin, Singh and Bansal,
+2020; Schuster, Fisch and Barzilay, 2021). Domain limited evidence for real-world claims is collected from problem-
+specific sources, such as academic articles for scientific claims (Kotonya and Toni, 2020b; Wadden, Lin, Lo, Wang, van
+Zuylen, Cohan and Hajishirzi, 2020), or specific evidence listed in fact-checking websites (Vlachos and Riedel, 2014;
+Hanselowski, Stab, Schulz, Li and Gurevych, 2019). Open-domain evidence for real-world claims is usually collected
+from the web via search engines (Popat et al., 2018; Augenstein et al., 2019).
+Recently, there has been more work considering evidence beyond free text. Such formats include structured or semi-
+structured forms of evidence. Sources include knowledge bases for structured form of evidence (Shi and Weninger,
+2016) and semi-structured evidence from semi-structured knowledge bases (Vlachos and Riedel, 2015), tabular data
+(Chen et al., 2019a; Gupta, Mehta, Nokhiz and Srikumar, 2020), and tables within a document (Aly, Guo, Schlichtkrull,
+Thorne, Vlachos, Christodoulopoulos, Cocarascu and Mittal, 2021).
+Additionally, there are some retrieval-specific datasets that aim at retrieving credible information from search
+engines (Clarke et al., 2020b). However, such tasks don’t incorporate claim checking as an explicit task.
+4Some of these datasets, such as the CheckThat! datasets, are partially crowd and partially expert annotated.
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+The State of Human-centered NLP Technology for Fact-checking
+4.3. Veracity Prediction
+Evidence retrieval and veracity prediction datasets are usually constructed jointly. Note, in some cases, evidence
+may be absent from the datasets. Veracity prediction datasets usually do not deal with claim detection or claim
+checkworthiness tasks separately. Instead, such datasets contain a set of claims that are either artificially constructed
+or collected from the internet.
+Artificial claims in veracity prediction datasets are often limited in scope and constructed for natural language
+reasoning research (Aly et al., 2021; Thorne et al., 2018; Schuster et al., 2021; Jiang et al., 2020; Chen, Wang, Chen,
+Zhang, Wang, Li, Zhou and Wang, 2019b). For example, FEVER (Thorne et al., 2018) and HoVer (Jacovi and Goldberg,
+2021) obtain claims from Wikipedia pages. Some datasets also implement subject-predicate-object triplets for fact-
+checking against knowledge bases (Kim and Choi, 2020; Shi and Weninger, 2016).
+Fact-checking websites are popular sources for creating veracity prediction datasets based on real claims. Several
+datasets obtain claims from either a single website or collect claims from many such websites and collate them (Wang,
+2017; Hanselowski et al., 2018; Augenstein et al., 2019; Vlachos and Riedel, 2014). Note that such claims are inherently
+expert annotated. Other sources of claims are social media (Potthast et al., 2018; Shu, Sliva, Wang, Tang and Liu,
+2017), news outlets (Horne, Khedr and Adali, 2018; Gruppi, Horne and Adalı, 2021; Nørregaard, Horne and Adalı,
+2019), blogs, discussions in QA forums, or similar user-generated publishing platforms (Mihaylova, Nakov, Màrquez,
+Barrón-Cedeño, Mohtarami, Karadzhov and Glass, 2018).
+Additionally some fact-checking datasets target domain-specific problems such as scientific literature (Wadden
+et al., 2020), climate change (Diggelmann, Boyd-Graber, Bulian, Ciaramita and Leippold, 2020), and public health
+(Kotonya and Toni, 2020b). Most datasets are monolingual but recent effort have started to incorporate multi-lingual
+claims (Gupta and Srikumar, 2021; Barnabò, Siciliano, Castillo, Leonardi, Nakov, Da San Martino and Silvestri, 2022).
+Early datasets focus on a binary veracity prediction - true or false (Mihalcea and Strapparava, 2009). Recent datasets
+often adopt an ordinal veracity labeling scheme that mimics fact checkering websites (Vlachos and Riedel, 2014; Wang,
+2017; Augenstein et al., 2019). However, every fact-checking website has a different scale for veracity, so datasets
+that span across multiple websites come with a normalization problem. While some datasets do not normalize the
+labels (Augenstein et al., 2019), some normalize them post-hoc (Kotonya and Toni, 2020a; Gupta and Srikumar, 2021;
+Hanselowski et al., 2019).
+4.4. Explanation
+While an explanation is tied to veracity prediction, only a few datasets explicitly address the problem of explainable
+veracity prediction (Atanasova et al., 2020a; Kotonya and Toni, 2020b; Alhindi, Petridis and Muresan, 2018). Broadly
+in NLP, often parts of the input is highlighted to provide an explanation for the prediction. This form of explanations
+is known as extractive rationale (Zaidan, Eisner and Piatko, 2007; Kutlu, McDonnell, Elsayed and Lease, 2020).
+Incorporating the idea of the extractive rationale, some datasets include a sentence from the evidence along with
+the label (Thorne et al., 2018; Hanselowski et al., 2018; Wadden et al., 2020; Schuster et al., 2021). Although such
+datasets do not explicitly define evidence as a form of explanation in such cases, the line between evidence retrieval and
+explanation blurs if the evidence is the explanation. However, explanations are different from evidence in a few ways.
+Particularly, explanations need to be concise for user consumption, while evidence can be a collection of documents
+or long documents. Explanations are user sensitive. Consequently, evidence alone as a form of explanation might have
+some inherent assumption about the user that might not be understandable for different groups of users (e.g., experts
+vs. non-experts).
+4.5. Challenges
+Claims Checkworthiness datasets are highly imbalanced, i.e., the number of checkworthy claims are relatively low
+compared to non-checkworthy claims (Williams, Rodrigues and Novak, 2020). Datasets are also not generalizable
+due to their limited domain-specific context (Guo et al., 2022). Additionally, while existing datasets cover various
+languages such as English, Arabic, Spanish, Bulgarian, and Dutch, they are primarily monolingual.
+Consequently,
+building multilingual checkworthiness predictors is still challenging. Much of the data in check-worthiness datasets is
+not originally intended to be used in classification. The criteria used by different organizations when selecting which
+claims to check is often subjective and may not generalize outside of the particular organization.
+Some annotation practices can result in artifacts in the dataset. For example, artificially constructed false claims,
+such as a negation-based false claim in FEVER, can lead to artifacts in models (Schuster et al., 2021). Models do not
+generalize well beyond the dataset because they might overfit to the annotation schema (Bansal, Nushi, Kamar, Lasecki,
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+The State of Human-centered NLP Technology for Fact-checking
+Weld and Horvitz, 2019). One way to identify such blind spots is by using adversarial datasets for fact-checking. Such
+a setting is incorporated in FEVER 2.0 (Thorne and Vlachos, 2019).
+Datasets constructed for research may not always capture how fact-checkers work in practice. This leads to
+limitations in the algorithms built on them. For example, interviews with fact-checkers report that they tend to consider
+a combination of contents of the posts and associated virality metrics (indicating reach) during fact-checking (Arnold,
+2020). However, most fact-checking datasets do not include virality metrics.
+Evidence Retrieval Some datasets have been constructed by using a claim verbatim as a query and taking the
+top search results as evidence. However, some queries are better than others for retrieving desired information.
+Consequently, greater care might be taken in crafting effective queries or otherwise improving evidence retrieval such
+that resulting datasets are more likely to contain quality evidence for veracity prediction. Otherwise, poor quality
+evidence becomes a bottleneck for the quality of the models trained at the later stages in the fact-checking pipeline
+(Singh et al., 2021).
+Veracity Prediction A key challenge in veracity prediction datasets is that the labels are not homogeneous across
+fact-checking websites and normalizing might introduce noise.
+Explanation Some datasets include entire fact-checking articles as evidence and their summaries as the form of
+explanation (Atanasova et al., 2020a; Kotonya and Toni, 2020b). In such cases, “explanation” components assume an
+already available fact-checking article. Instead, providing abstractive summaries and explaining the reasoning process
+over the evidence would be more valuable.
+Data Generation Recent years have seen an increasing interest in the use of data generation and data augmentation
+for various NLP tasks (Liu, Swayamdipta, Smith and Choi, 2022; Hartvigsen, Gabriel, Palangi, Sap, Ray and Kamar,
+2022; Dhole, Gangal, Gehrmann, Gupta, Li, Mahamood, Mahendiran, Mille, Srivastava, Tan et al., 2021; Kovatchev,
+Smith, Lee and Devine, 2021). The use of synthetic data has not been extensively explored in the context of fact-
+checking.
+5. Automating Fact-checking
+NLP research in automated fact-checking has primarily focused on building models for different automated fact-
+checking tasks utilizing existing datasets. In the following section, we highlight the broad modeling strategies employed
+in the literature, with more detailed discussion related to explainable methods for automated fact-checking.
+5.1. General NLP Capabilities
+Claim Detection and Checkworthiness While claim detection is usually implemented as a classification task only,
+claim checkworthiness is typically implemented both as ranking (Nakov et al., 2021a) and classification task (Zeng
+et al., 2021). As discussed earlier in the task formulation Section (3.1), the broad task of claim detection can be
+broken down into sub-tasks of identifying claims, filtering duplicate claims, and prioritizing claims based on their
+checkworthiness. Another instance of identifying claims is detecting rumors in social media streams.
+Some early works in rumor detection focus on feature engineering from available metadata the text itself (Enayet
+and El-Beltagy, 2017; Aker, Derczynski and Bontcheva, 2017; Zhou, Jain, Phoha and Zafarani, 2020). More advanced
+methods for claim detection involve LSTM and other sequence models (Kochkina, Liakata and Augenstein, 2017).
+Such models are better at capturing the context of the text (Zubiaga, Liakata, Procter, Wong Sak Hoi and Tolmie,
+2016). Tree-LSTM (Ma, Gao and Wong, 2018) and Hierarchical attention networks (Guo, Cao, Zhang, Guo and Li,
+2018) capture the internal structure of the claim or the context in which the claim appears. Additionally, graph neural
+network approaches can capture the related social media activities along with the text (Monti, Frasca, Eynard, Mannion
+and Bronstein, 2019).
+Similarly, early works in claim-checkworthiness utilize support vector machines using textual features and rank
+the claims in terms of their priorities (Hassan et al., 2017a). For example, Konstantinovskiy et al. (2021) build a
+classification model for checkworthiness by collapsing the labels to checkable vs. non-checkable claim. They build
+a logistic regression model that uses word embeddings along with syntax based features (parts of speech tags, and
+named entities). Neural methods such as LSTM performed well in earlier checkworthiness shared tasks (Elsayed,
+Nakov, Barrón-Cedeño, Hasanain, Suwaileh, Da San Martino and Atanasova, 2019). Additionally, Atanasova, Nakov,
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+The State of Human-centered NLP Technology for Fact-checking
+Màrquez, Barrón-Cedeño, Karadzhov, Mihaylova, Mohtarami and Glass (2019b) show that capturing context helps
+with the checkworthiness task as well. Models such as RoBERTa obtained higher performance in the later edition of the
+CheckThat! shared task (Williams et al., 2020; Martinez-Rico, Martinez-Romo and Araujo, 2021) for English language
+claims. Fine-tuning such models for claim detection tasks has become more prevalent for claim checkworthiness in
+other languages as well (Hasanain and Elsayed, 2020; Williams et al., 2020).
+Filtering previously fact-checked claims is a relatively new task in this domain. Shaar et al. (2020) propose an
+approach using BERT and BM-25 to match claims against fact-checking databases for matching claims with existing
+databases. Additionally, fine-tuning RoBERTa on various fact-checking datasets resulted in high performance for
+identifying duplicate claims (Bouziane, Perrin, Cluzeau, Mardas and Sadeq, 2020). Furthermore, a combination of
+pretrained model Sentence-BERT and re-ranking with LambdaMART performed well for detecting previously fact-
+checked claims (Nakov et al., 2021b).
+Evidence Retrieval and Veracity Prediction Evidence retrieval and veracity prediction in the pipeline can be
+modeled sequentially or jointly. Similar to claim detection and checkworthiness models, early works use stylistic
+features and metadata to arrive at veracity prediction without external evidence (Wang, 2017; Rashkin et al., 2017).
+Models that include evidence retrieval often use commercial search APIs or some retrieval approach such as TF-IDF,
+and BM25 (Thorne et al., 2018). Similar to question-answering models, some works adopt a two-step approach. First
+a simpler model (TF-IDF or BM-25) is used at scale and then a more complex model is used for re-ranking after the
+initial pruning (Thorne et al., 2018; Nie, Wang and Bansal, 2019; Hanselowski et al., 2019). Additionally, document vs.
+passage retrieval, or 2-stage “telescoping” approaches, are adopted where the first stage is retrieving related documents
+and the second stage is to retrieve the relevant passage. Two stage approaches are useful for scaling up applications
+as the first stage is more efficient than the second stage. For domain specific evidence retrieval, using domain-bound
+word embeddings has been shown to be effective (Zeng et al., 2021).
+The IR task is not always a part of the process. Instead, it is often assumed that reliable evidence is already available.
+While this simplifies the fact-checking task so that researchers can focus on veracity prediction, in practice evidence
+retrieval is necessary and cannot be ignored. Moreover, in practice one must contend with noisy (non-relevant), low
+quality, and biased search results during inference.
+As discussed earlier in Section 3.3, assessing the reliability of gathered evidence may be necessary. If the evidence is
+assumed to be trustworthy, then it suffices to detect the stance of each piece of evidence and then aggregate (somehow)
+to induce veracity (e.g., perhaps assuming all evidence is equally important and trustworthy). However, often one
+must contend with evidence “in the wild” of questionable reliability, in which case assessing the quality (and bias) of
+evidence is an important precursor to using it in veracity prediction.
+Veracity prediction utilizes textual entailment for inferring veracity over either a single document as evidence
+or over multiple documents. Dagan, Dolan, Magnini and Roth (2010) define textual entailment as “deciding, given
+two text fragments, whether the meaning of one text is entailed (can be inferred) from another text.” Real-world
+applications often require reasoning over multiple documents (Augenstein et al., 2019; Kotonya and Toni, 2020b;
+Schuster et al., 2021). Reasoning over multiple documents can be done either by concatenation (Nie et al., 2019) or
+weighted aggregation (Nguyen et al., 2018b). Weighted aggregation virtually re-ranks the evidence considered to filter
+out the unreliable evidence (Ma, Gao, Joty and Wong, 2019; Pradeep, Ma, Nogueira and Lin, 2021). Some approaches
+also use Knowledge Bases as the central repository of all evidence (Shi and Weninger, 2016). However, evidence is
+only limited to what is available in the knowledge base (Guo et al., 2022; Zeng et al., 2021). Moreover, a fundamental
+limitation of knowledge bases is that not all knowledge fits easily into structured relations.
+Recent developments in large language models help extend the knowledge base approach. Fact-checking models
+can rely on pretrained models to provide evidence for veracity prediction (Lee, Li, Wang, Yih, Ma and Khabsa, 2020).
+However, this approach can encode biases present in the language model (Lee, Bang, Madotto and Fung, 2021).
+An alternative approach is to help fact-checkers with downstream tasks by processing evidence. An example of
+such work is generating summaries over available evidence using BERT (Fan et al., 2020).
+Limitations With the recent development of large, pre-trained language models and deep learning for NLP, we see
+a significant improvement across the fact-checking pipeline. With the introduction of FEVER (Thorne et al., 2018;
+Thorne, Vlachos, Cocarascu, Christodoulopoulos and Mittal, 2019; Aly et al., 2021) and CheckThat! (Nakov et al.,
+2021b) we have benchmarks for both artificial and real-life claim detection and verification models. However, even
+the state-of-the-art NLP models perform poorly on the benchmarks above. For example, the best performing model on
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+FEVER 2018 shared task (Thorne et al., 2018) reports an accuracy of 0.675. Models perform worse on multi-modal
+shared task FEVEROUS (Aly et al., 2021): the best performing model reports 0.56 accuracy score6. Similarly, the best
+checkworthiness model only achieved an average precision of 0.65 for Arabic claims and 0.224 for English claims in
+the CheckThat! 2021 shared task for identifying checkworthiness in tweets (Nakov et al., 2021b). On the other hand,
+the best performing model for identifying check-worthy claims in debates reports 0.42 average precision. Surprisingly,
+Barrón-Cedeño et al. (2020), the top performing model for checkworthiness detection, report an average precision of
+0.806 (Williams et al., 2020). . For the fact-checking task of CheckThat! 2021 (Nakov et al., 2021b), the best performing
+model reports a 0.83 macro F1 score. However, the second-best model only reports a 0.50 F1 score. Given this striking
+gap in performance between the top system vs. others, it would be valuable for future work to benchmark systems on
+additional datasets in order to better assess the generality of these findings.
+It is not easy to make a direct comparison between different methods that are evaluated in different settings and
+with different datasets (Zeng et al., 2021). Moreover, the pipeline design of automated fact-checking creates potential
+bottlenecks, e.g., performance on the veracity prediction task on most datasets is dependent on the claim detection task
+performance or the quality of the evidence retrieved. Extensive benchmarks are required to incorporate all of the prior
+subtasks in the fact-checking pipeline systematically (Zeng et al., 2021).
+Much of AI research is faced with a fundamental trade-off between working with diverse formulations of a problem
+and standardized benchmarks for measuring progress. This trade-off also impacts automated fact-checking research.
+While there exist benchmarks such as FEVER and the CheckThat!, most models built on those benchmarks may
+not generalize well in a practical setting. Abstract and tractable formulations of a problem may help us develop
+technologies that facilitate practical adoption. However, practical adoption requires significant engineering effort
+beyond the research setting. Ideally, we would like to see automated fact-checking research continue to move toward
+increasingly realistic benchmarks while incorporating diverse formulations of the problem.
+5.2. Explainable Approaches
+Although the terms interpretability and explainability are often used interchangeably, and some times defined to be
+so (Molnar, 2020), we distinguish interpretability vs. explainability similar to (Kotonya and Toni, 2020a). Specifically,
+interpretability represents methods that provide direct insight into an AI system’s components (such as features and
+variables), often requiring some understanding of the specific to the algorithm, and often built for expert use cases
+such as model debugging. Explainability represents methods to understand an AI model without referring to the actual
+component of the systems. Note that, in the task formulation section, we have also talked about explaining veracity
+prediction. The goal of such explanation stems from fact-checker needs to help readers understand the fact-checking
+verdict. Therefore, explaining veracity prediction aligns more closely with explainability over interpretability. When
+the distinction between explainability vs. interpretability does not matter, we follow Vaughan and Wallach (2020) in
+adopting intelligibility (Vaughan and Wallach, 2020) as an umbrella term for both concepts.
+Sokol and Flach (2019) propose a desiderata for designing user experience for machine learning applications.
+Kotonya and Toni (2020a) extend them in the context of fact-checking and suggest eight properties of intelligibility:
+actionable, causal, coherent, context-full, interactive, unbiased or impartial, parsimonious, and chronological.
+Additionally, there are three dimensions specifically for explainable methods in NLP (Jacovi and Goldberg, 2020):
+1. Readability: are explanations clear?
+2. Plausibility: are explanations compelling or persuasive?
+3. Faithfulness: are explanations faithful to the model’s actual reasoning process?
+In comparison with the available intelligibility methods in NLP (Wiegreffe and Marasovic, 2021), only a few
+are applied to existing fact-checking works. Below, we highlight only commonly observed explainable fact-checking
+methods (also noted by Kotonya and Toni (2020a)).
+Attention-based Intelligibility Despite the debate about attention being a reliable intelligibility method (Jain and
+Wallace, 2019; Wiegreffe and Pinter, 2019; Serrano and Smith, 2019; Bibal, Cardon, Alfter, Wilkens, Wang, François
+and Watrin, 2022), it remains a popular method in existing deep neural network approaches in fact-checking. Attention-
+based explanations are provided in various forms:
+1. highlighting tokens in articles (Popat et al., 2018)
+5https://fever.ai/2018/task.html
+6https://fever.ai/task.html
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+2. highlighting salient excerpts from evidence utilizing comments related to the post (Shu et al., 2019)
+3. n-gram extraction using self-attention (Yang, Pentyala, Mohseni, Du, Yuan, Linder, Ragan, Ji and Hu, 2019)
+4. attention from different sources other than the claim text itself, such as the source of tweets, retweet propagation,
+and retweeter properties (Lu and Li, 2020)
+Rule discovery as explanations Rule mining is a form of explanation prevalent in knowledge base systems (Gad-
+Elrab et al., 2019; Ahmadi, Lee, Papotti and Saeed, 2019). These explanations can be more comprehensive, but as noted
+in the previous section, not all statements can be fact-checked via knowledge-based methods due to limitations of the
+underlying knowledge-base itself. Some approaches provide general purpose rule mining in an attempt to address this
+limitation (Ahmadi, Truong, Dao, Ortona and Papotti, 2020).
+Summarization as explanations Both extractive and abstractive summaries can provide explanations for fact-
+checking. Atanasova et al. (2020a) provides natural language summaries to explain the fact-checking decision. They
+explore two different approaches - explanation generation and veracity prediction as separate tasks, and joint training
+of the both. Joint training performs worse than single training. Kotonya and Toni (2020b) combine abstractive and
+extractive approaches to provide a novel summarization approach. Brand, Roitero, Soprano, Rahimi and Demartini
+(2018) show jointly training prediction and explanation generation with encoder-decoder models such as BART (Lewis,
+Liu, Goyal, Ghazvininejad, Mohamed, Levy, Stoyanov and Zettlemoyer, 2020) results in explanations that help the
+crowd to perform better veracity assessment.
+Counterfactuals and adversarial methods Adversarial attacks on opaque models help to identify any blind-spots,
+biases and discover data artifacts in models (Ribeiro, Wu, Guestrin and Singh, 2020). Shared task FEVER 2.0 (Thorne
+et al., 2019) asked participants to devise methods for generating adversarial claims to identify weaknesses in the fact-
+checking methods. Natural language generation models such as GPT-3 can assist in formulating adversarial claims.
+More control over the generation can come from manipulating the input to natural language generation methods
+and constraining the generated text within original vocabulary (Niewiński, Pszona and Janicka, 2019). Atanasova,
+Wright and Augenstein (2020b) generate claims with n-grams inserted into the input text. Thorne and Vlachos (2019)
+experiment with several adversarial methods such as rule-based adversary, semantically equivalent adversarial rules
+(or SEARS) (Ribeiro, Singh and Guestrin, 2018), negation, and paraphrasing-based adversary. Adversarial attacks
+are evaluated based on the potency (correctness) of the example and reduction in system performance. While methods
+such as SEARS and paraphrasing hurt the system performance, hand-crafted adversarial examples have higher potency
+score.
+Interpretable methods (non-BlackBox) Some fact-checking works use a white-box or inherently interpretable
+model for fact-checking. Nguyen et al. (2018b,a) utilize a probabilistic graphical model and build an interactive
+interpretable model for fact-checking where users are allowed to directly override model decisions. Kotonya,
+Spooner, Magazzeni and Toni (2021) propose an interpretable graph neural network for interpretable fact-checking
+on FEVEROUS dataset (Aly et al., 2021).
+Limitations Intelligible methods in NLP and specifically within fact-checking are still in their infancy. Analysis of
+Kotonya and Toni (2020a) shows that most methods do not fulfill the desiderata mentioned earlier in this section.
+Specifically, they find that none of the existing models meet the criteria of being actionable, causal, and chronological.
+They also highlight that no existing method explicitly analyzes whether explanations are impartial. Some forms
+of explanations, such as rule-based triplets, are unbiased as they do not contain sentences or contain fragments of
+information (Kotonya and Toni, 2020a).
+Some explainable methods address a specific simplified formulation of the task. For example, Kotonya and Toni
+(2020b); Atanasova et al. (2020a) both assume that expert-written fact-checking articles already exist. They provide
+explanations as summaries of the fact-checking article. However, in practice, a fact-checking system would not have
+access to such an article for an unknown claim.
+In the case of automated fact-checking, most intelligible methods focus on explaining the outcome rather than
+describing the process to arrive at the outcome (Kotonya and Toni, 2020a). Moreover, all of the tasks in the fact-
+checking pipeline have not received equal attention for explainable methods. Kotonya and Toni (2020a) also argue
+that automatic fact-checking may benefit from explainable methods that provide insight into how outcomes of earlier
+sub-task in the fact-checking pipeline impact the outcome of later subtasks.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 13 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+Most explainable NLP works evaluate explanation quality instead of explanation utility or faithfulness. Jacovi
+and Goldberg (2020) argue for a thorough faithfulness evaluation for explainable models. For example, even though
+attention-based explanations may provide quality explanations, they may not necessarily be faithful. Moreover, expla-
+nation utility requires separate evaluation by measuring whether explanations improve both i) human understanding
+of the model (Hase and Bansal, 2020) and ii) human effectiveness of the downstream task (Nakov et al., 2021a).
+Additionally, most intelligible methods establish only one-way communication from the model to humans. Instead,
+explanations might improve the model and human performance by establishing a bidirectional feedback loop.
+5.3. Human-in-the-loop Approaches
+Human-in-the-loop (HITL) approaches can help scale automated solutions while utilizing human intelligence for
+complex tasks. There are different ways of applying HITL methods, e.g., delegating sub-tasks to crowd workers
+(Demartini, Trushkowsky, Kraska, Franklin and Berkeley, 2013; Demartini, Difallah and Cudré-Mauroux, 2012;
+Sarasua, Simperl and Noy, 2012), active learning (Settles, 2009; Zhang, Lease and Wallace, 2017), interactive machine
+learning (Amershi, Cakmak, Knox and Kulesza, 2014; Joachims and Radlinski, 2007), and decision support systems
+where humans make the final decision based on model outcome and explanations (Zanzotto, 2019).
+While HITL approaches in artificial intelligence are prevalent, only a few recent works employ such approaches
+in fact-checking. HITL approaches are predominantly more present in the veracity prediction task than other parts of
+the pipeline. For example, Demartini et al. (2020) propose a HITL framework for combating online misinformation.
+However, they only consider hybrid approaches for two sub-tasks in the fact-checking pipeline: a) claim check-
+worthiness and b) truthfulness judgment (same as veracity prediction). Below, we discuss the existing HITL approaches
+by how the system leverages human effort for each sub-task in the fact-checking pipeline.
+Claim Detection, Checkworthiness, and Prioritization Social media streams are often monitored for rumors as
+a part of the claim detection task (Guo et al., 2022). Farinneya, Abdollah Pour, Hamidian and Diab (2021) apply an
+active learning-based approach at the claim detection stage for identifying rumors on social media. In-domain data is
+crucial for traditional supervised methods to perform well for rumor detection (Ahsan, Kumari and Sharma, 2019), but
+in real-world scenarios, sufficient in-domain labeled data may not be available in the early stages of development. A
+semi-supervised approach such as active learning is beneficial for achieving high performance with fewer data points.
+Empirical results shows that Tweet-BERT, along with the least confidence-based sample selection approach, performs
+on par with supervised approaches using far less labeled data (Farinneya et al., 2021).
+Similarly, Tschiatschek, Singla, Gomez Rodriguez, Merchant and Krause (2018) propose a HITL approach that
+aims to automatically aggregate user flags and recommend a small subset of the flagged content for expert fact-checking.
+Their Bayesian inference-based approach jointly learns to detect fake news and identify the accuracy of user flags over
+time. One strength of this approach is that the algorithm improves over time in identifying users’ flagging accuracy.
+Consequently, over time this algorithm’s performance improves. This approach is also robust against spammers. By
+running the model on publicly available Facebook data where a majority of the users are adversarial, experiments show
+that their algorithm still performs well.
+Duke’s Tech & Check team implemented HITL at the claim check-worthiness layer (Adair and Stencel, 2020). To
+avoid flagging false check-worthy claims, a human expert would sort claims detected by ClaimBuster (Hassan, Zhang,
+Arslan, Caraballo, Jimenez, Gawsane, Hasan, Joseph, Kulkarni, Nayak et al., 2017b), filter out the ones deemed more
+important for fact-checkers, and email them to several organizations. In essence, this approach helped fact-checkers
+prioritize the claims to check through an additional level of filtering. Currently, several published fact-checks on
+PolitiFact were first alerted by the emails from Tech & Check.
+Note that the CheckThat! (Nakov et al., 2021b; Shaar et al., 2021b; Barrón-Cedeño et al., 2020; Atanasova, Nakov,
+Karadzhov, Mohtarami and Da San Martino, 2019a) is a popular shared task for claim detection, check-worthiness, and
+prioritization tasks. However such shared tasks often have no submissions that employs HITL methodologies. Shared
+tasks for HITL approaches could encourage more solutions that can complement the limitations of model-only based
+approaches.
+Evidence Retrieval and Veracity Prediction Most work in HITL fact-checking caters to veracity prediction, and
+only a few consider evidence retrieval as a separate task. While there is a body of literature on HITL approaches in
+information retrieval (Chen and Jain, 2013; Demartini, 2015), we know of no work in that direction for fact-checking.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 14 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+Shabani, Charlesworth, Sokhn and Schuldt (2021) leverage HITL approaches for providing feedback about claim
+source, author, message, and spelling (SAMS). Annotators answer four yes/no questions about whether the article has
+a source, an author, a clear and unbiased message, and any spelling mistake. Furthermore, this work integrates the
+features provided by humans in a machine learning pipeline, which resulted in a 7.1% accuracy increase. However,
+the evaluation is performed on a small dataset with claims related to Covid-19. It is unclear if this approach would
+generalize outside of the domain. Moreover, further human effort can be reduced in this work by automating spell-
+check and grammar-check. SAMS could be quite limited in real life situations as most carefully crafted misinformation
+often looks like real news. Model generated fake news can successfully fool annotators (Zellers, Holtzman, Rashkin,
+Bisk, Farhadi, Roesner and Choi, 2020), and thus SAMS might also fail to flag such fake news.
+Qu, Barbera, Roitero, Mizzaro, Spina and Demartini (2021a) and Qu, Roitero, Mizzaro, Spina and Demartini
+(2021b) provide an understanding of how human and machine confidence scores can be leveraged to build HITL
+approaches for fact-checking. They consider explicit self-reported annotator confidence and compute implicit con-
+fidence based on standard deviation among ten crowd workers. Model confidence is obtained from bootstrapping
+(Efron and Tibshirani, 1985) ten different versions of the model and then computing standard deviation over the scores
+returned by the soft-max layer. Their evaluation shows that explicit crowd and model confidence are poor indicators
+of accurate classification decisions. Although the crowd and the model make different mistakes, there is no clear
+signal that confidence is related to accuracy. However, they show that implicit crowd confidence can be a useful
+signal for identifying when to engage experts to collect labels. A more recent study shows that a politically balanced
+crowd of ten is correlated with the average rating of three fact-checkers (Allen, Arechar, Pennycook and Rand, 2020).
+Gold, Kovatchev and Zesch (2019) also find that annotations by a crowd of ten correlate with the judgments of three
+annotators for textual entailment, which is utilized by veracity prediction models.
+A series of studies show that the crowd workers can reliably identify misinformation (Roitero, Soprano, Fan, Spina,
+Mizzaro and Demartini, 2020a; Roitero, Soprano, Portelli, Spina, Della Mea, Serra, Mizzaro and Demartini, 2020b;
+Soprano, Roitero, La Barbera, Ceolin, Spina, Mizzaro and Demartini, 2021). Furthermore, Roitero et al. (2020b) show
+that crowd workers not only can identify false claims but also can retrieve proper evidence to justify their annotation.
+One weakness of this study is that it only asks users to provide one URL as evidence. However, in practice, fact-
+checking might need reasoning over multiple sources of information. Although these studies do not propose novel
+HITL solutions, they provide sufficient empirical evidence and insights about where crowd workers can be engaged
+reliably in the fact-checking pipeline.
+Nguyen et al. (2018b) propose joint modeling of crowd annotations and machine learning to detect the veracity of
+textual claims. The key strength of the model is that it assumes all annotators can make mistakes, which is a possibility
+as fact-checking is a difficult task. Another strength is that this model allows users to import their knowledge into the
+system. Moreover, this HITL approach can collect on-demand stance labels from the crowd and incorporate them in
+veracity prediction. Empirical evaluation shows that this approach achieves strong predictive performance. A follow-up
+study provides an interactive HITL tool for fact-checking (Nguyen et al., 2018a).
+Nguyen, Weidlich, Yin, Zheng, Nguyen and Nguyen (2020) propose a HITL system to minimise user effort and cost.
+Users validate algorithmic predictions but do so at a minimal cost by only validating the most-beneficial predictions
+for improving the system. This system provides a guided interaction to the users and incrementally gets better as users
+engage with it.
+It is important to note that research on crowdsourcing veracity judgment is at an early stage. Different factors such
+as demographics, political leaning, criteria for determining the expertise of the assessors (Bhuiyan, Zhang, Sehat and
+Mitra, 2020), cognitive factors (Kaufman, Haupt and Dow, 2022), and even the rating scale (La Barbera, Roitero,
+Demartini, Mizzaro and Spina, 2020) led to different levels of alignment with expert ratings. Bhuiyan et al. (2020)
+outline research directions for designing better crowd processes specific to different types of misinformation for the
+successful utilization of crowd workers.
+Explaining Veracity Prediction HITL systems in fact-checking often use veracity explanations to correct model
+errors. As discussed earlier, Nguyen et al. (2018a) provides an interpretable model that allows users to impart their
+knowledge when the model is wrong. Empirical evaluation shows that users could impart their knowledge into the
+system. Similarly, Zhang, Rudra and Anand (2021b) propose a method that collects user feedback from explanations.
+Note that this method explains veracity prediction outcomes based on the evidence retrieved and their stance. Users
+provide feedback in terms of stance and relevance of the retrieved evidence. The proposed approach employs lifelong
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 15 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+learning which enables the system to improve over time. Currently there is no empirical evaluation of this system to
+identify the effectiveness of this approach.
+Although natural language generation models are getting increasingly better (Radford, Wu, Child, Luan, Amodei,
+Sutskever et al., 2019), generating abstractive fact-checking explanations is still in its infancy (Kotonya and Toni,
+2020b). HITL methods could be leveraged to write reports justifying fact-checking explanations.
+Limitations After reviewing existing HITL approaches across different fact-checking tasks, we also list out several
+limitations as follow. First, some HITL approaches adopt several interpretable models to integrate human input, but
+the resulting models do not perform as well as the state-of-the-art deep learning models (Nguyen et al., 2018b,a).
+Farinneya et al. (2021) apply HITL approaches to scale up rumor detection from a limited amount of annotated data.
+Although it performs well to generalize the algorithm for a new topic in a few-shot manner, one of the weaknesses
+is that data from other domains or topics causes a high variance in model performance. Consequently, in-domain
+model performance might degrade when out-of-domain data is introduced in model training. This issue may hinder the
+model’s generalizability in practice, especially where a clear demarcation between topic domains may not be possible.
+More importantly, there is a lack of empirical studies on how to apply HITL approaches of fact-checking for
+practical adoption. Although HITL approaches provide a mechanism to engage human in the process of modeling
+development, several human factors, such as usability, intelligibility, and trust, become important to consider when
+applying this method in the real-world use case. Fact-checking is a time-sensitive task and requires expertise to process
+complex information over multiple sources (Graves, 2017). Fact-checkers and policy makers are often skeptical about
+any automated or semi-autoamted solutions as this type of research requires human creativity and expertise (Arnold,
+2020; Micallef et al., 2022). Therefore, more empirical evidence needs to be found to assess the effectiveness of
+applying different HITL approaches to automated fact-checking.
+6. Existing Tools for Fact-checking
+In the previous section, we reviewed the details of current NLP technologies for fact-checking. Subsequently,
+we extend our review of automated fact-checking to the HCI literature and discuss existing practices of applying fact-
+checking into real-world tools that assist human fact-checkers. In brief, there is a lack of holistic review of fact-checking
+tools from a human-centered perspective. Additionally, we found that the articulation of work between human labor and
+AI tools is still opaque in this field. Research questions include but are not limited to: 1) how can NLP tools facilitate
+human work in different fact-checking tasks? 2) how can we incorporate user needs and leverage human expertise to
+inform the design of automated fact-checking?
+In this section, we examine current real-world tools that apply NLP technologies in different stages of fact-checking
+and clarify the main use cases of these tools. We argue that more research concerning human factors for building
+automated fact-checking, such as user research, human-centered design, and usability studies, should be conducted to
+improve the practical adoption of automated fact-checking. These studies help us identify the design space of applying
+explainable and HITL approaches for real-world NLP technologies.
+6.1. Claim Detection and Prioritization
+The first step in claim detection is sourcing content to possibly check. On end-to-end encrypted platforms, such
+as WhatsApp, Telegram, and Signal, crowdsourcing-based tip-lines play a vital role in identifying suspicious content
+that is not otherwise accessible (Kazemi et al., 2021b). As another example, Check from Meedan 7, a tip-line service
+tool, also helps fact-checkers monitor fake news for in-house social media. User flagging of suspect content on social
+media platforms such as Facebook is also a valuable signal for identifying such content, and crowdsourcing initiatives
+like Twitter’s BirdWatch can further help triage and prioritize claims for further investigation.
+In the stage of finding and choosing claims to check, fact-checkers assess the fact-checking related quality of a
+claim and decide whether to fact-check it (Graves, 2017; Micallef et al., 2022). NLP models in claim detection, claim
+matching, and check-worthiness are useful to assist the above decision-making process. However, integrating them
+into real-world tools that help fact-checkers prioritize what to check requires more personalized effort. Graves (2018a)
+points out that it is important to design the aforementioned models to cater to fact-checker organizational interests,
+stakeholder needs, and changing news trends.
+7https://meedan.com/check
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 16 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+As one of the fact-checking qualities of a claim, checkability can be objectively analyzed by whether a claim
+contains one or more purported facts that can be verified (Section 3.1). Fact-checkers find it useful to apply models
+that identify checkable claims to their existing workflow because the model helps them filter irrelevant content and
+claims that are uncheckable when they are choosing claims to check (Arnold, 2020). ClaimBuster, one of the well-
+known claim detection tools, is built to find checkable claims from a large scale of text content (Hassan et al., 2017a).
+Claim detection can also be integrated into speech recognition tools to spot claims from live speech (Adair, 2020).
+Additionally, if a claim has already been fact-checked, fact-checkers can skip it and prioritize claims that have
+not been checked. As a relatively new NLP task, claim matching has been integrated into some current off-the-shelf
+search engines or fact-checking tools to help fact-checkers find previously fact-checked claims. For example, Google
+Fact Check Explorer8 can retrieve previously fact-checked claims by matching similar fact-check content to user input
+queries. Similarly, with Meedan’s Check, if users send a tip with fake news that has been previously fact-checked, the
+tool further helps fact-checkers retrieve the previous fact-check and send it to users.
+Whether or not to fact-check a claim depends on an organization’s goals and interests. Tools built for claim detection
+need to take such interests into account. For example, Full Fact developed a claim detection system that classifies
+claims into different categories, such as quantity, predictions, correlation or causation, personal experience, and laws
+or rules of operations (Konstantinovskiy et al., 2021). The claim categories are designed by their fact-checkers to cater
+to their needs of fact-checking UK political news in a live fact-checking situation. Identifying certain claims, such as
+quantity, correlation or causation, might be particularly useful for fact-checkers to evaluate the credibility of politician
+statements and claims. The system also helps tailor fact-checkers’ downstream tasks, such as fact-check assignments
+and automated verification for statistical claims (Nakov et al., 2021a).
+Fact-checkers also use social media monitoring tools to find claims to check, such as CrowdTangle, TweetDeck, and
+Facebook’s (unnamed) fact-checking tool, but those tools are not very effective to detect checkable claims. Some fact-
+checkers reported that only roughly 30% of claims flagged by Facebook’s fact-checking tool were actually checkable
+(Arnold, 2020). A low hanging fruit is to integrate claim detection models into these social media monitoring tools so
+that it is easier for fact-checkers to identify claims that are both viral and checkable. Additionally, these tools should
+enable fact-checkers to locate certain figures, institutions, or agencies according to their fact-checking interests and
+stakeholder needs so that these tools can better identify and prioritize truly check-worthy claims. An important question
+in implementing those systems is how to measure the virality of a claim and its change over time.
+It would also be useful to integrate veracity prediction into previous fact-checking tools because fact-checkers
+may pay the most attention to claims9 that are suspect and uncertain (since obviously true or false claims likely do
+not require a fact-check). However, information or data points that are used to give such predictions should also be
+provided to fact-checkers. If sources, evidence, propagation patterns, or other contextual information that models use
+to predict claim veracity can be explained clearly for fact-checkers, they can also triage these indicators to prioritize
+claims more holistically.
+6.2. Tools for Evidence Retrieval
+After finding and prioritizing which claim to check, fact-checkers investigate claims following three main activities:
+1) decomposing claims, 2) finding evidence, and 3) tracing the provenance of claims and their spread. Note that these
+three activities are intertwined with each other by using different information-seeking tools in the fact-checking process.
+Fact-checkers search for evidence by decomposing claims into sub questions. Evidence found while investigating a
+claim may further modify or add to the sub-questions (Singh et al., 2021). By iteratively investigating claims via
+online search, fact-checkers reconstruct the formation and the spread of a claim to assess its truth (Graves, 2017). In
+this section, we discuss the utility of existing information-seeking tools, including off-the-shelf search engines and
+domain-specific databases, that assist fact-checkers in each activity.
+Claim decomposition is not a specific activity that qualitative researchers have reported or analyzed in their fact-
+checking studies, but we can find more details from where fact-checking organizations describe their methodology10
+and how fact-checkers approach complex claims in their fact-checks11. Claim decomposition refers to how fact-
+checkers interpret ambiguous terms of a claim and set the fact-checking boundaries to find evidence. Decomposing
+8https://toolbox.google.com/factcheck/explorer
+9https://www.factcheck.org/our-process/
+10https://leadstories.com/how-we-work.html
+11https://www.factcheck.org/2021/10/oecd-data-conflict-with-bidens-educational-attainment-claim/ In this fact-
+check, fact-checkers decompose what President Biden mean by “advanced economies” and “young people”. The approach of defining these two
+terms directly influence their fact-checking results.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 17 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+claims effectively requires sensitive curiosity and news judgments for fact-checkers that are cultivated through years
+of practice. Unfortunately, we are not aware of any existing tools that facilitate this process.
+Traditional methodology to decompose claims is to ask sub-questions. Recent NLP studies simulate this process
+by formulating it as a question-answering task (Fan et al., 2020; Chen et al., 2022). Researchers extract justifications
+from existing fact-checks and crowdsource sub-questions to decompose the claim. For automated-fact-checking, this
+NLP task might be very beneficial to improve the performance of evidence retrieval by auto-decomposing claims into
+smaller checkable queries (Chen et al., 2022). Although it is difficult for NLP to match the abilities of professional
+fact-checkers, it might help scale up the traditional, human fact-checking process. It could also help the public, new
+fact-checkers, or journalists to more effectively investigate complex claims and search for evidence.
+How fact-checkers find evidence is usually a domain-specific reporting process, contacting experts or looking for
+specific documents from reliable sources (Graves, 2017; Micallef et al., 2022). Instead of conducting random searches
+online, most fact-checkers include a list of reliable sources in which to look for evidence. Tools that are designed for
+searching domain datasets can also help fact-checkers to find evidence. For example, Li, Fang, Lou, Li and Zhang
+(2021) built an analytical search engine for retrieving the COVID-19 news data and summarizing it in an easy to
+digest, tabular format. The system can decompose analytical queries into structured entities and extract quantitative
+facts from news data. Furthermore, if evidence retrieval is accurate enough for in-domain datasets, the system can
+take a leap further to auto-verify domain-related claims. We provide more detailed use cases of veracity prediction in
+Section 6.3.
+Fact-checkers mainly use off-the-shelf search engines, such as Google, Bing, etc., to trace a claim’s origin from
+publicly accessible documents (Beers, Haughey, Melinda, Arif and Starbird, 2020; Arnold, 2020). Other digital
+datasets, such as LexisNexis and InternetArchive, are also useful for fact-checkers to trace claim origin. To capture the
+formation and change of a claim, search engines should not only filter unrelated content, but also retrieve both topically
+and evidentially relevant content. Hasanain and Elsayed (2021) report that most topically relevant pages retrieved from
+Google do not contain evidential information, such as statistics, quotes, entities, or other types of facts. Additionally,
+most built-in search engines in social media platforms, such as Twitter and Facebook, only filter “spreadable” content
+not “credible” content (Alsmadi, Alazzam and AlRamahi, 2021).
+Furthermore, these off-the-shelf search engines do not support multilingual search, so it is difficult for fact-checkers
+to trace claims if they are translated from other languages (Graves, 2017; Nakov et al., 2021a). NLP researchers have
+started to use multilingual embedding models to represent claim-related text in different languages and match existing
+fact-checks (Kazemi, Gaffney, Garimella and Hale, 2021a). This work not only helps fact-checkers find previously
+fact-checked claims more easily from other languages, but also to examine how the claim is transformed and reshaped
+by the media in different languages and socio-political contexts.
+6.3. Domain-specific Tools for Claim Verification
+As discussed in Sections 3.2 and 3.3, most verification prediction models are grounded on the collected evidence
+and the claim. To build an end-to-end claim verification system, NLP developers need to construct domain-specific
+datasets incorporating both claims and evidence. Different from complex claims that contain multiple arguments and
+require decomposition, claims that have simple linguistic structure with purported evidence or contain statistical facts
+can be automatically verified (Nakov et al., 2021a).
+Karagiannis, Saeed, Papotti and Trummer (2020) built CoronaCheck, a search engine that can directly verify Covid-
+19 related statistical claims by retrieving official data curated by experts (Dong, Du and Gardner, 2020). Full Fact
+(The Poynter Institute, 2021) also took a similar approach to verify statistical macroeconomic claims by retrieving
+evidence from UK parliamentary reports and national statistics. Additionally, Wadden et al. (2020) built a scientific
+claim verification pipeline by using abstracts that contain evidence to verify a given scientific claim.
+However, pitfalls still exist if fact-checkers use these domain-specific verification tools in practice. For example, the
+CoronaCheck tool cannot check the claim “The Delta variant causes more death than the Alpha variant” simply because
+the database does not contain fine-grained death statistics for Covid variants. Additionally, checking a statistical or
+scientific claim might only be a part of the process of checking a more complex claim, which requires fact-checkers
+to contextualize the veracity of previous statistical or scientific checks. In general, domain-specific tools are clearly
+valuable to use when available, though in practice they are often incomplete and insufficient on their own to check
+complex claims.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 18 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+7. Discussion
+In this review, we have 1) horizontally outlined the research of applying NLP technologies for fact-checking from
+the beginning of task formulation to the end of tools adoption; as well as 2) vertically discussing the capabilities and
+limitations of NLP for each step of a fact-checking pipeline. We perceive a lack of research that bridges both to assist
+fact-checkers. Explainable and HITL approaches leverage both human and computational intelligence from a human-
+centered perspective, but there is a need to provide actionable guides to utilize both methods for designing useful
+fact-checking tools. In this section, we propose several research directions to explore the design space of applying
+NLP technologies to assist fact-checkers.
+7.1. Distributing Work between Human and AI for Mixed-initiative Fact-checking
+The practice of fact-checking has already become a type of complex and distributed computer-mediated work
+(Graves, 2018a). Although Graves (2017) breaks down a traditional journalist fact-checking pipeline into five steps,
+the real situation of fact-checking a claim is more complicated (Juneja and Mitra, 2022). Various AI tools are adopted
+dynamically and diversely by fact-checkers to complete different fact-checking tasks (Arnold, 2020; Beers et al., 2020;
+Micallef et al., 2022).
+Researchers and practitioners increasingly believe that future fact-checking should be a mixed-initiative practice
+in which humans perform specific tasks while machines take over others (Nguyen et al., 2018a; Lease, 2020; Nakov
+et al., 2021a). To embed such hybrid and dynamic human-machine collaborations into existing fact-checking workflow,
+the task arrangement between human and AI need to be articulated clearly by understanding the expected outcomes
+and criteria for each. Furthermore, designing a mixed-initiative tool for different fact-checking tasks requires a more
+fine-grained level of task definition for human and AI (Lease, 2018, 2020). In Section 5.3, we discuss several studies
+highlighting the role of humans in the fact-checking workflow, e.g., a) human experts select check-worthy claims from
+claim detection tools (Hassan et al., 2017b) and deliver them to fact-checkers, b) ask crowd workers to judge reliable
+claims sources (Shabani et al., 2021), or c) flag potential misinformation (Roitero et al., 2020b) to improve veracity
+prediction. All of the above human activities are examples of micro-tasks within a mixed-initiative fact-checking
+process.
+Prior work in crowdsourcing has shown that it is possible to effectively break down the academic research process
+and utilize crowd workers to partake in smaller research tasks (Vaish, Davis and Bernstein, 2015; Vaish, Gaikwad,
+Kovacs, Veit, Krishna, Arrieta Ibarra, Simoiu, Wilber, Belongie, Goel et al., 2017). Given this evidence, we can
+also break down sub-tasks of a traditional fact-checking process into more fine-grained tasks. Therefore, key research
+questions include: a) How can we design these micro-tasks to facilitate each sub-task of fact-checking, and b) What
+are the appropriate roles for human and AI in different micro-tasks?
+To effectively orchestrate human and AI work, researchers need to understand the respective roles of human and AI,
+and how they will interact with one another, because it will directly affect whether humans decide to take AI advice
+(Cimolino and Graham, 2022). Usually, if AI aims to assist high-stake decision-making tasks, such as recidivism
+prediction (Veale, Van Kleek and Binns, 2018) and medical treatments (Cai, Winter, Steiner, Wilcox and Terry, 2019),
+considerations of risk and trust will be important factors for people to adopt such AI assistants (Lai, Chen, Liao,
+Smith-Renner and Tan, 2021). In the context of fact-checking, if AI directly predicts the verdict of a claim, fact-
+checkers may be naturally skeptical about how the AI makes such a prediction (Arnold, 2020). On the other hand, if
+AI only helps to filter claims that are uncheckable, such as opinions and personal experience, fact-checkers may be
+more willing to use such automation with less concern about how AI achieves it. Deciding whether a claim is true or
+false is a high-stake decision-making task for fact-checkers, while filtering uncheckable claims is a less important but
+tedious task that fact-checkers want automation to help with. Therefore, the extent of human acceptance of AI varies
+according to how humans assess the task assigned to AI, resulting in different human factors, such as trust, transparency,
+and fairness. Researchers need to specify or decompose these human factors into different key variables that can be
+measured during the model development process. Given a deep understanding of the task relationship between human
+and AI, researchers can then ask further research questions on how to apply an explainable approach, or employ a
+HITL system vs. automated solutions, to conduct fact-checking. Here we list out several specific research topics that
+contain mixed-initiative tasks, including: a) assessing claim difficulty leveraging crowd workers, b) breaking down a
+claim into a multi-hop reasoning task and engaging the crowd to find information relevant to the sub-claims, and c)
+designing micro-tasks to parse a large number of documents retrieved by web search to identify sources that contain
+the evidence needed for veracity prediction.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 19 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+7.2. Human-centered Evaluation of NLP Technology for Fact-Checkers
+We begin this section by proposing key metrics from human factors for evaluating systems (i.e., what to measure
+and how to measure them): accuracy, time, model understanding, and trust (Section 7.2.1). Following this, we further
+propose a template for an experimental protocol for human-centered evaluations in fact-checking (Section 7.2.2).
+7.2.1. Metrics
+Accuracy Most fact-checking user studies assume task accuracy as the primary user goal (Nguyen et al., 2018a;
+Mohseni, Yang, Pentyala, Du, Liu, Lupfer, Hu, Ji and Ragan, 2021). Whereas non-expert users (i.e., social media users
+or other form of content consumers) might be most interested in the veracity outcome along with justification, fact-
+checkers often want to use automation and manual effort interchangeably in their workflow (Arnold, 2020; Nakov et al.,
+2021a). Thus, we need a more fine-grained approach towards measuring accuracy beyond the final veracity prediction
+accuracy. For fine-grained accuracy evaluation, it is also crucial to capture fact-checker accuracy, particularly for the
+sub-tasks for which they use the fact-checking tool.
+With the assumption that “ground truth” exists for all of the sub-tasks in the fact-checking pipeline, accuracy can
+be computed by comparing user answers with the ground truth. Note that measuring sub-task level accuracy is trickier
+than end-to-end fact-checking accuracy. Sub-task level accuracy can be captured by conducting separate experiments
+for each sub-task. Suppose the point of interest is to understand user performance for detecting claim-checkworthiness.
+In that case, we will need to collect additional data specific to the claim-checkworthiness task.
+In some cases, it is possible to merge multiple sub-tasks for evaluation purposes. For example, Miranda, Nogueira,
+Mendes, Vlachos, Secker, Garrett, Mitchel and Marinho (2019) evaluate the effectiveness of their tool with journalists
+by capturing the following two key variables: a) the relevance of retrieved evidence, and b) the accuracy of the predicted
+stance. This method provides essential insight into evidence retrieval, stance detection, and the final fact-checking task.
+Depending on the tool, the exact detail of this metric will require specific changes according to tool affordances.
+Note that both time and accuracy measures need to control for claim properties. For example, if a claim has been
+previously fact-checked, it would take less time to fact-check such claims. On the other hand, a new claim that is more
+difficult to assess would require more time.
+Model Understanding Fact-checkers want to understand the tools they use. For example, Arnold (2020) pointed
+out that fact-checkers expressed a need for understanding CrowdTangle’s algorithm for detecting viral content on
+various social media platforms. Similarly, Nakov et al. (2021a) observed a need for increased system transparency in
+the fact-checking tools used by different organizations. Lease (2018) argues that transparency is equally important for
+non-expert users to understand the underlying system and make an informed judgement. Although this is not a key
+variable related to user performance, it is important for practical adoption.
+To measure understanding, users could be asked to self-report their level of understanding on a Likert-scale.
+However, simply asking participants if they understand the algorithm is not a sufficient metric. For example, it does not
+indicate whether participants will be able to simulate tool behavior (Hase and Bansal, 2020). We suggest the following
+steps for measuring model understanding based on prior work (Cheng, Wang, Zhang, O’Connell, Gray, Harper and
+Zhu, 2019).
+1. Decision Prediction. To capture users’ holistic understanding of a tool, users could be provided claims and
+asked the following: “What label would the tool assign to this claim?”
+2. Alternative Prediction. Capturing how changes in the input influence the output can also measure understand-
+ing, e.g., by asking users how the tool would assign a label to a claim when input parts are changed. Imagine a
+tool that showed the users the evidence it has considered to arrive at a veracity conclusion. Now, if certain pieces
+of evidence were swapped, how would that be reflected in the model prediction?
+Trust For practical adoption, trust in a fact-checking tool is crucial across all user groups. While model understanding
+is often positively correlated with trust, understanding alone may not suffice to establish trust. In this domain, fact-
+checkers and journalists may have less trust in algorithmic tools (Arnold, 2020). On the other hand, there is also the risk
+of over-trust, or users blindly following model predictions (Nguyen et al., 2018a; Mohseni et al., 2021). To maximize
+the tool effectiveness, we would want users to neither dismiss all model predictions out of hand (complete skepticism)
+nor blindly follow all model predictions (complete faith). Instead, it is important to calibrate user trust for the most
+effective tool usage. We suggest measuring a notion of calibrated trust (Lee and See, 2004): how often users abide by
+correct model decisions and override erroneous model decisions.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 20 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+Both
+Model
+User
+Neither
+User Prediction
+Correct
+Incorrect
+Correct
+Incorrect
+Model 
+Prediction
+Figure 2: Confusion Matrix for User Predictions vs. Model Predictions with respect to ground truth (gold). We assume
+model predictions are provided to the user, who then decides whether to accept or reject the model prediction. The top-left
+quadrant (Both) covers cases where users correctly follow model predictions. The top-right quadrant (Model) denotes the
+cases where the model is correct but users mistakenly reject the model decisions. The bottom-left quadrant User denotes
+the cases where users correctly reject erroneous model predictions. The bottom-right quadrant Neither denotes the cases
+where users incorrectly accept erroneous model predictions. Quantifying user vs. model predictions in this manner enables
+measurement of calibrated trust: how often users abide by correct model decisions and override erroneous model decisions.
+To measure calibrated trust, we imagine a confusion matrix shown in Figure 2. The rows denote correct vs. incorrect
+model predictions while the columns denote correct vs. incorrect user predictions. A user who blindly followed all
+model predictions would have their behavior entirely captured by the main (primary) diagonal, whereas a user who
+skeptically rejected all model predictions would have their behavior captured entirely in the secondary diagonal. The
+ideal user’s behavior would be entirely captured in the first column: accepting all correct model predictions and rejecting
+all incorrect model predictions. To promote effective human-AI teaming, AI tools should assist their human users in
+developing strong calibrated trust to appropriately trust and distrust model predictions as each case merits.
+Beyond calibrated trust, one could also measure quantitative trust by adopting methodologies from the human-
+machine trust literature (Lee and Moray, 1992). For example, Cheng et al. (2019) adapted prior work into a 7-point
+Likert scale. A similar scale can be reused for evaluating trust in a fact-checking tool. For example, we can create five
+different Likert-scales to measure the agreement (or disagreement) of users with the following statements:
+• I understand the fact-checking tool.
+• I can predict how the tool will behave.
+• I have faith that the tool would be able to cope with the different fact-checking task.
+• I trust the decisions made by the tool.
+• I can count on the tool to provide reliable fact-checking decisions.
+Additional factors Individual differences among users might result in substantial variation in experimental outcomes.
+For example, varying technical literacy (Cheng et al., 2019), any prior knowledge about the claims, and users’ political
+leaning (Thornhill, Meeus, Peperkamp and Berendt, 2019) might influence user performance on the task while using
+fact-checking tools. Thus it is valuable to capture these factors in study design. For example:
+1. Technical Literacy: Users’ familiarity with popular technology tools (e.g, recommendation engines, spam
+detectors) and their programming experience (Cheng et al., 2019) as well as familiarity with existing fact-
+checking tools.
+2. Media Literacy: Users’ familiarity with 1) the fact-checking process, and 2) fact-checks from popular
+organizations such as PolitiFact and FactCheck.Org.
+3. Demographics: Users’ education level, gender, age, and political leaning.
+Quantitative measures alone are not sufficient as they do not capture certain nuances about how effectively a tool
+integrates into a fact-checker’s workflow. For example, even if users understand and trust the working principle of a
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 21 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+tool, it is unclear why they do so. Hence, users might be asked a few open-ended questions at the end of the study to
+gather qualitative insights. Such questions could include:
+1. Describe your understanding of the tool. Do any specific aspects of its design seem to assist or detract from your
+understanding of how it works?
+2. Why do you trust or not trust the tool?
+3. Would you use this tool beyond this study, and if so, in what capacity?
+7.2.2. Experimental Protocol
+One strategy to capture the aforementioned metrics is to design a mixed-methods study. Here we outline the
+template for such a study. Imagine the goal were to measure the user performance for fact-checking using a new tool
+(let’s call it tool A) compared to an existing tool (tool B). Fact-checking tasks in the real world might be influenced by
+user priors about the claims being checked. Thus, a within-subject study protocol may be more appropriate to account
+for such priors (Shi, Bhattacharya, Das, Lease and Gwizdka, 2022).
+1. Pre-task: Users would first be asked to fact-check a set of claims. To do so, first a user would be asked to leverage
+a pre-existing tool B at this stage. Tool B can be replaced with different baselines, depending on the particular
+use case, ranging from simple web-search by non-expert users to proprietary tools used by fact-checkers and
+journalists. Users would be asked to think aloud at this stage.
+2. Learning: At this stage users would familiarize themselves with the new tool (tool A). Users would need to
+fact-check a different set of claims from the first one. Ground truth would also accessible to the user to form a
+prior about what kind of mistakes a tool might make. Claims here would be selected at random to reflect tool
+capabilities. Moreover, tool performance metrics would be given to the users as additional information. Users
+would be encouraged to ask questions about the tool at this stage.
+3. Prediction: Users would now be asked to fact-check the same claims from step-1 above but this time they are
+asked to leverage the tool A. Users would be asked to think out loud through this stage. Users could simply guess
+the answers and achieve a high accuracy score. Thus, claims selected for stages (1) & (3) would be a balanced
+set of claims with an equal distribution of true positive, true negative, false positive, and false negative samples.
+This idea is adopted from prior work (Hase and Bansal, 2020).
+4. Post-task survey: Users would now be asked to take a small survey for capturing trust, understanding, technical
+literacy, media literacy, and demographic information.
+5. Post-task interview: Upon completion of these steps, users would be interviewed with open-ended questions to
+gather insights about their understanding and trust in the system.
+The measures and study protocol could be useful in the context of evaluating any new fact-checking system compared to
+an existing system or practices. Specifics might vary depending on the target user group and the tool’s intended purpose.
+Above we use the whole fact-checking pipeline to illustrate our experimental protocol. However this technique can be
+applied to other sub-tasks of automated fact checking, granted that we have the ground truth of the outcome for that
+sub-task. For example, let us assume a new claim detection tool has been proposed that takes claims from a tip-line
+(Kazemi et al., 2021b). Currently, fact-checkers use an existing claim-matching algorithm to filter out the already
+fact-checked claim. Now, if we replace tool B above with the existing claim-matching algorithm and tool A with the
+proposed claim detection tool, we can utilize the protocol mentioned above. In conclusion, one could evaluate how
+users perform for claim detection tasks using the new tool compared to the existing ones in terms of their accuracy,
+time, understanding, and trust.
+While we have proposed an ideal, extensive version of an evaluation protocol for evaluating new fact-checking tools,
+note that the actual protocol used in practice could be tailored according to the time required from the participants and
+the cost of conducting the experiment.
+8. Conclusion
+This review highlights the practices and development of the state-of-the-art in using NLP for automated fact-
+checking, emphasizing both the advances and the limitations of existing task formulation, dataset construction, and
+modeling approaches. We partially discuss existing practices of applying these NLP tasks into real-world tools that
+assist human fact-checkers. In recent years we have seen significant progress in automated fact-checking using NLP.
+Anubrata Das et al.: Preprint submitted to Elsevier
+Page 22 of 30
+
+The State of Human-centered NLP Technology for Fact-checking
+A broad range of tasks, datasets, and modeling approaches have been introduced in different parts of the fact-checking
+pipeline. Moreover, with recent developments in transformers and large language models, the model accuracy has
+improved across tasks. However, even state-of-the-art models on existing benchmarks — such as FEVER and CLEF!
+— may not yet be ready for practical adoption and deployment.
+To address these limitations, we advocate development of hybrid, HITL systems for fact-checking. As a starting
+point, we may wish to reorient the goals of existing NLP tasks from full automation towards decision support. In
+contrast with fully-automated systems, hybrid systems instead involve humans-in-the-loop and facilitate human-AI
+teaming (Bansal et al., 2019; Bansal, Wu, Zhou, Fok, Nushi, Kamar, Ribeiro and Weld, 2021b; Bansal, Nushi, Kamar,
+Horvitz and Weld, 2021a). Such use of hybrid systems can help a) scale-up human decision making; b) augment
+machine learning capabilities with human accuracy; and c) mitigate unintended consequences from machine errors.
+Additionally, we need new benchmarks and evaluation practices that can measure how automated and hybrid systems
+can improve downstream human accuracy (Smeros, Castillo and Aberer, 2021; Fan et al., 2020) and efficiency in
+fact-checking.
+Acknowledgements
+This research was supported in part by the Knight Foundation, the Micron Foundation, Wipro, and by Good
+Systems12, a UT Austin Grand Challenge to develop responsible AI technologies. The statements made herein are
+solely the opinions of the authors and do not reflect the views of the sponsoring agencies.
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