Base_JSON/prefixH/json/hcinlp/2022.hcinlp-1.7.json

{
    "paper_id": "2022",
    "header": {
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        "date_generated": "2023-01-19T03:35:31.733860Z"
    },
    "title": "Narrative Datasets through the Lenses of NLP and HCI",
    "authors": [
        {
            "first": "Sharifa",
            "middle": [],
            "last": "Sultana",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Cornell University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Renwen",
            "middle": [],
            "last": "Zhang",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "National University of Singapore",
                "location": {}
            },
            "email": "r.zhang@nus.edu.sg"
        },
        {
            "first": "Hajin",
            "middle": [],
            "last": "Lim",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Seoul National University",
                "location": {}
            },
            "email": ""
        },
        {
            "first": "Maria",
            "middle": [],
            "last": "Antoniak",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Cornell University",
                "location": {}
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
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    "abstract": "In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widelyused narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.",
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        "abstract": [
            {
                "text": "In this short paper, we compare existing value systems and approaches in NLP and HCI for collecting narrative data. Building on these parallel discussions, we shed light on the challenges facing some popular NLP dataset types, which we discuss these in relation to widelyused narrative-based HCI research methods; and we highlight points where NLP methods can broaden qualitative narrative studies. In particular, we point towards contextuality, positionality, dataset size, and open research design as central points of difference and windows for collaboration when studying narratives. Through the use case of narratives, this work contributes to a larger conversation regarding the possibilities for bridging NLP and HCI through speculative mixed-methods.",
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                "section": "Abstract",
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                "text": "Human beings are myth-makers; we use stories and imagination to create communities and make sense of the world and our place in it (Bamberg and Georgakopoulou, 2008) . Narratives are powerful modes of expression, with physical, emotional, and social benefits for both the narrator and the audience (Pennebaker and Beall, 1986; Pennebaker, 1997; Merz et al., 2014; Oh and Kim, 2016; Tangherlini, 2000) . They can also be powerful methods for understanding human behavior and beliefs (Golsteijn and Wright, 2013) .",
                "cite_spans": [
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                        "start": 131,
                        "end": 165,
                        "text": "(Bamberg and Georgakopoulou, 2008)",
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                        "start": 298,
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                        "text": "(Pennebaker and Beall, 1986;",
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                        "start": 327,
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                        "text": "Pennebaker, 1997;",
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                        "start": 345,
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                        "text": "Merz et al., 2014;",
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                        "start": 364,
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                        "text": "Oh and Kim, 2016;",
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                        "start": 382,
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                        "text": "Tangherlini, 2000)",
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "Crucially, narratives are situated; they are told and take place in specific social contexts (Piper et al., 2021) . Natural language processing (NLP) methods can analyze patterns across large datasets, putting stories into context. But narrative datasets in NLP are often removed from the narratives' original contexts (e.g., scraped internet datasets) or are designed without any explicit context or social grounding (e.g., short and artificial stories).",
                "cite_spans": [
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                        "start": 93,
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                        "text": "(Piper et al., 2021)",
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                        "text": "(NLP)",
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                "section": "Introduction",
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                "text": "In contrast, contextuality is of the utmost importance in qualitative human-computer interaction (HCI) approaches to narrative. HCI researchers frequently borrow social science methods including surveys, interviews, focus groups, and ethnography for closer investigations that address the diversity of human life and experiences (Bruner, 1987; Golsteijn and Wright, 2013) . Qualitative HCI methods are often constrained to small sample sizes and susceptible to observer biases, but narrative research and portraiture methods enable creative and holistic engagement with participants' experiences and meaning-making processes (Williams, 1984; Wright and McCarthy, 2004; Bardzell et al., 2012) .",
                "cite_spans": [
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                        "start": 329,
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                        "text": "(Bruner, 1987;",
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                        "text": "Golsteijn and Wright, 2013)",
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                        "start": 625,
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                        "text": "(Williams, 1984;",
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                        "text": "Bardzell et al., 2012)",
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                "section": "Introduction",
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                "text": "These differences make narrative datasets an useful case study when considering tensions and possible collaborations between NLP and HCI. Both disciplines face challenges in their study and analysis of narrative. While NLP datasets contain a high volume of data points, their labels are constrained to a specific task; in contrast, smaller HCI datasets, in particular data collected through qualitative methods such as ethnography and interview, are open-ended in research scope but situated in a particular context. Combining these methods can contribute to designing multifaceted datasets while not losing the sight of individual experiences and perspectives in a large volume of stories.",
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                "section": "Introduction",
                "sec_num": "1"
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                "text": "In the following sections, we outline dominant framings of narrative and narrative dataset collection in NLP and HCI. Placing these framings side-by-side highlights a set of tensions-including dataset size, contextuality and positionality, and dataset design-that we finally consider as material for synthesis and mixed methods approaches to narrative data.",
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                "section": "Introduction",
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            {
                "text": "In a recent overview of NLP and humanist approaches to \"narrative understanding\", Piper et al. (2021) formulate narrativity as a scalar construct rather than a binary class; texts can include some or all narrative features (e.g., narrator, audience, se-quential actions). Most NLP narrative tasks focus on building abstractions from narratives by extracting these features and measuring relationships among them. These tasks include extracting narrative structure, like scripts, plot units, or narrative arcs (Schank and Abelson, 1977; Lehnert, 1981; Jurafsky, 2008, 2009; Goyal et al., 2010; Reagan et al., 2016) ; modeling connections between characters (Bamman et al., 2013; Iyyer et al., 2016; Lukin et al., 2016) ; generating new stories or summaries (Goldfarb-Tarrant et al., 2020; Guan et al., 2020; Akoury et al., 2020) ; answering questions about the story (Richardson et al., 2013) , and identifying a correct story ending (Chambers and Jurafsky, 2008; Mostafazadeh et al., 2016) .",
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                        "text": "Lehnert, 1981;",
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                        "text": "Jurafsky, 2008, 2009;",
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                        "text": "Goyal et al., 2010;",
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                        "text": "Reagan et al., 2016)",
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                        "text": "(Bamman et al., 2013;",
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                        "text": "Iyyer et al., 2016;",
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                        "text": "Lukin et al., 2016)",
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                        "text": "(Chambers and Jurafsky, 2008;",
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                        "text": "Mostafazadeh et al., 2016)",
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                "section": "NLP Framings of Narrative",
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            {
                "text": "As in other areas of NLP, some narrative research falls into shared tasks, where artificial story datasets are often (though not always) used for testing a particular technical ability of a system. These datasets are sometimes created and often labeled by crowdworkers, and they include brief scenarios not explicitly connected to broader social contexts and narratives. For example, one of the widely used corpora for testing performance on the Story Cloze task is ROCStories dataset which is a collection of 100,000 crowdsourced \"five-sentence commonsense stories\" (Mostafazadeh et al., 2016) .",
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                "text": "Narrative research in NLP also includes corpusbased studies, where researchers use narrative models to learn about a particular dataset and its authors. Corpus-based studies depend on curated datasets that range widely, e.g., fictional works (e.g., novels, fairytales) (Jans et al., 2012; Iyyer et al., 2016) , news stories (Chambers and Jurafsky, 2008) , biographies (Bamman and Smith, 2014) , and personal stories shared orally or on social media (Gordon and Swanson, 2009; Ouyang and McKeown, 2014; Antoniak et al., 2019) . These curated datasets were authored in social contexts separate from the NLP research study and are gathered afterwards. Curated datasets can also be used for shared tasks, e.g., coreference resolution (Bamman et al., 2020) , story generation (Akoury et al., 2020 ).",
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                        "text": "(Bamman et al., 2020)",
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                "text": "There are a small number of naturalistic NLP narrative datasets that lie outside of the above categories. For example, Sap et al. (2020) collected autobiographical stories and retellings of these stories from crowdworkers; this data was shared as part of the research study but was also grounded in the authors' personal experiences.",
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                "text": "And finally, many modern NLP methods for nar-ratives rely on large, pretrained models (Devlin et al., 2019) . These models are trained on massive and (mostly) undocumented datasets, containing a mixture of documents from unrelated domains to generalize to other domains and tasks (after finetuning). These pretraining datasets, like the aptlynamed Pile (Gao et al., 2020) , are too large for full datasheet descriptions and can encode human biases (Bender et al., 2021) .",
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                        "text": "(Devlin et al., 2019)",
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                        "text": "(Gao et al., 2020)",
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            {
                "text": "Four key themes are associated with HCI's sensibility of narrative: (a) fact (universal/objective truth) (b) experience (global, local, and day-to-day experiences) (c) interpretation (perceived understanding of a and b) (d) fiction (imaginations and cultural value-system based storytelling) (Bruner, 1990; Sterling, 2009; Golsteijn and Wright, 2013) . HCI researchers often ask questions to understand problems better and care about accuracy, legitimacy, and materiality (i.e., why and how certain issues are important) of information. Many subdomains of HCI refer to and build on users' experiences regarding narratives (Feuston and Piper, 2019) . HCI practitioners' and designers' interactions with social settings and/or professional environments frequently influence their experiences in a given time and situation, and so they consciously refrain from making generalizable statements and encourage the mention of contextuality, which is strongly associated with the narratives (Golsteijn and Wright, 2013) . Experience-centered research also values empathy to understand the researchers' orientation to the user, and whether they are motivated to empathize with the users' needs and emotional responses (Wright and McCarthy, 2008) .",
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                        "text": "Sterling, 2009;",
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                "section": "HCI Framings of Narrative",
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            {
                "text": "HCI uses both qualitative and quantitative techniques to gather and examine narratives. Both quantitative techniques (e.g., surveys and computational analysis of social media data) and qualitative approaches (e.g., interviews, observation, and focus groups) and artistic techniques are frequent in HCI. In this paper, we focus on qualitative HCI methods for narratives, as they differ from NLP approaches in terms of ontology and epistemology, representing two distinct worldviews (Slevitch, 2011) .",
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                "text": "In surveys, researchers conduct statistical analyses and evaluate the responses based on standard tests and sets of metrics. Using qualitative text coding in cases of free-text responses within the survey is also common. More specifically, HCI scholars have conducted surveys to investigate people's motivations, goals, and challenges with regard to posting narratives on social media (Sannon et al., 2019) , as well as factors that influence their decision-making when it comes to online disclosure (Bazarova et al., 2015; Zhang et al., 2021) These approaches are motivated by understanding a specific set of people in a specific setting and social context. Ethnography, observations, interviews, focus group discussions, and story-telling are common methods used by qualitative HCI researchers (Sultana and Ahmed, 2019). Other than transcriptionbased qualitative text coding, image, audio and video coding are also used for analysis (Andalibi et al., 2017; Sharma and De Choudhury, 2015) . In this regard, not only the texts, images, audio, and videos of the participants are used, but also pictures of the environment, background noise, and even reasons and results of unintended interruptions can contribute to the richness of narratives.",
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                        "text": "(Bazarova et al., 2015;",
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                        "text": "Zhang et al., 2021)",
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                "section": "HCI Framings of Narrative",
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            {
                "text": "HCI researchers also use curated social media data and conduct statistical analyses and qualitative text coding on this data to understand certain research problems. HCI researchers in some domains also use online ethnographic techniques on social media and collaborative platforms (e.g., gaming, e-commerce) to grow deeper understandings of these communities (Mim and Ahmed, 2020).",
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                "sec_num": "3"
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                "text": "Many HCI researchers and participants use artistic techniques like drawings and installations for addressing research queries (Sturdee et al., 2021) . It is quite common for artists to conduct an autoethnography with themselves, in which their creation of art is their narrative. In many cases, such narratives are symbolic and contextual.",
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                        "start": 126,
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                        "text": "(Sturdee et al., 2021)",
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                "section": "HCI Framings of Narrative",
                "sec_num": "3"
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                "text": "Finally, many HCI researchers adopt mixed methods where they use both qualitative and quantitative approaches to grow a wider and deeper understanding of their research problems. For example, a recent feminist-HCI design used a survey to understand the spread of gender harassment on social media and also conducted interviews and focus group discussions for participatory design and user evaluation of Unmochon (Sultana et al., 2021) .",
                "cite_spans": [
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                "section": "HCI Framings of Narrative",
                "sec_num": "3"
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            {
                "text": "Volume and depth of narratives. On one hand, NLP techniques can analyze more narratives (and often more normalized) with reduced researcher workload, though at the loss of qualitative detail.",
                "cite_spans": [],
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                "section": "Interdisciplinary Tensions",
                "sec_num": "4"
            },
            {
                "text": "On the other hand, qualitative methods in HCI offer a deeper understanding of narratives based on a more limited sample size. Despite smaller datasets, HCI often depends on theoretical saturation of narratives, in which all important themes are represented, while in NLP, even if the number of datapoints is greater, researchers interested in a particular community often rely on an extracted sample decided by someone else (i.e., curated datasets) which might not capture all relevant themes.",
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                "section": "Interdisciplinary Tensions",
                "sec_num": "4"
            },
            {
                "text": "While gaining holistic understandings of an individual in HCI, researchers care about participants' life experiences, social relationships, and observable artifacts surrounding them. In NLP research, it is often impossible to glean such relevant and detailed information from individuals in a large dataset, where abstraction rather than situatedness is the goal. This contrast also pertains to the agency and privacy of the narrative sources-whether authors are informed about and consent to the inclusion of their narrative in the dataset for research purposes-as well as the uncertain representation of different groups. Narrative data in NLP often lacks explicit context (artificial datasets) or is used out of context (curated and massive datasets); naturalistic datasets generated specifically for the NLP study are more rare (Sap et al., 2020) , unlike in HCI. For example, it is common in NLP to scrape data passively that was written in a different context than the research study, as opposed to interview studies in HCI, where researchers explicitly collect stories for the current study. However, NLP datasets are often designed tasks that model abstractions, like common narrative arcs, where simplified datasets can help researchers tackle specific tasks.",
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                        "text": "(Sap et al., 2020)",
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                "section": "Abstraction and Contextuality",
                "sec_num": "4.1"
            },
            {
                "text": "HCI's emphasis on contextuality opens rather than constrains research possibilities: when narratives are collected in HCI, the emphasis is on high-level and open-ended research goals, focusing on discovering things that have not been explored sufficiently or that might even be in conflict with the researchers' assumptions. Similarly, when labeling themes in narratives, multiple HCI researchers are involved in an open coding process, in which independent coders develop their own themes before combining and refining these themes to ensure the validity and reliability of their interpretations. In contrast, shared narrative tasks in NLP rely on labeled datasets intended for one task, whose data and labels are meant to model one specific concept (like story conclusions) that the researchers already hold, even if multiple annotators are involved.",
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                "section": "Closed and Open Dataset Design.",
                "sec_num": "4.2"
            },
            {
                "text": "We argue that mixed methods research, drawing from both NLP and HCI, could allow for richer narrative datasets and more holistic understandings of narrative and the social impacts of narrative. This triangulation of methods not only minimizes the biases of researchers and enhances the validity of the findings, but also reveals different dimensions of the phenomenon being investigated (McQueen and Knussen, 2002) . While prior work has suggested mixed methods in many other NLP contexts (e.g., grounded topic modeling (Baumer et al., 2017)), narratives are particularly well-suited because of the strong research interest on both sides and the tensions enumerated above.",
                "cite_spans": [
                    {
                        "start": 387,
                        "end": 414,
                        "text": "(McQueen and Knussen, 2002)",
                        "ref_id": "BIBREF34"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Towards a Mixed Methods Approach",
                "sec_num": "5"
            },
            {
                "text": "Prior work has suggested various frameworks to select between mixed methods approaches (Heuer and Buschek, 2021; Inie and Derczynski, 2021) . These mixed methods may follow different design patterns, including explanatory, exploratory, parallel, and nested methods (Creswell and Clark, 2017). Therefore, the choice of study design should be guided by research questions and goals. For example, if researchers aim to understand the structural patterns of a certain type of narrative (e.g., mental health disclosures on social media) and examine its situatedness (i.e., audiences and context), they might consider an explanatory sequential mixed methods design, where researchers first use quantitative methods to analyze scraped data followed by qualitative interviews and selected narratives in social context. Contrarily, to understand how individuals frame a particular event or phenomenon (e.g., the COVID-19 pandemic) and see if that frame can be applied to a larger population, researchers might opt for an exploratory sequential mixed methods design, characterized by an initial qualitative phase of data collection and analysis, followed by quantitative analysis drawing on a larger dataset.",
                "cite_spans": [
                    {
                        "start": 87,
                        "end": 112,
                        "text": "(Heuer and Buschek, 2021;",
                        "ref_id": "BIBREF27"
                    },
                    {
                        "start": 113,
                        "end": 139,
                        "text": "Inie and Derczynski, 2021)",
                        "ref_id": "BIBREF28"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Customizing an Approach",
                "sec_num": "5.1"
            },
            {
                "text": "Because situatedness or contextuality are essential components of narrative, contextuality can act as a bridging frame in these mixed methods de-signs, to move between the volume of narrative data in NLP and the depth of analysis in qualitative HCI methods. Researchers can move between \"zooming in\" on specific stories using qualitative methods and \"zooming out\" to analyze larger patterns across stories (rather than just one or the other). For example, HCI methods can be used to gather qualitative detail about a dataset's context, while research methods and tools from NLP can help HCI researchers situate smaller datasets within their larger-scale, cross-community contexts (Zhang et al., 2017; Lucy and Bamman, 2021) . Both sets of methods can also help address how platform design, moderation, and other contextual features shape the sharing of narratives online.",
                "cite_spans": [
                    {
                        "start": 680,
                        "end": 700,
                        "text": "(Zhang et al., 2017;",
                        "ref_id": "BIBREF60"
                    },
                    {
                        "start": 701,
                        "end": 723,
                        "text": "Lucy and Bamman, 2021)",
                        "ref_id": "BIBREF32"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Contextuality",
                "sec_num": "5.2"
            },
            {
                "text": "Qualitative HCI methods emphasize reflexivity and positionality. These practices can encourage NLP researchers to recognize the inherent biases in their research questions, datasets, modeling architectures, procedures, and interpretation of results. Narrative tasks are not simple; each instance usually has multiple right answers, and researchers need to be aware of their own biases in evaluation. For example, when selecting appropriate story endings, annotators are not operating with a \"view from nowhere\" but from particular values and circumstances (Nagel, 1989) . The positionality of the researchers should also be considered in relation to the narrative authors; the authors' positions are often lost in NLP datasets, even when those datasets are labeled with internal states (e.g., sentiment) known only to the authors. Classifying internal states carries risks (Stark, 2018) , which are compounded in the study of personal narratives, where affect, relationships, and narratival motivations are intertwined. One strategy to address this challenge is to include the narrative authors in the dataset design (Heuer and Buschek, 2021) . And NLP methods can be used to explore the authors' and researchers' positionality by comparing biased linguistic patterns (Bolukbasi et al., 2016; Caliskan et al., 2017) contained in narratives and case notes.",
                "cite_spans": [
                    {
                        "start": 556,
                        "end": 569,
                        "text": "(Nagel, 1989)",
                        "ref_id": "BIBREF38"
                    },
                    {
                        "start": 873,
                        "end": 886,
                        "text": "(Stark, 2018)",
                        "ref_id": "BIBREF51"
                    },
                    {
                        "start": 1117,
                        "end": 1142,
                        "text": "(Heuer and Buschek, 2021)",
                        "ref_id": "BIBREF27"
                    },
                    {
                        "start": 1268,
                        "end": 1292,
                        "text": "(Bolukbasi et al., 2016;",
                        "ref_id": "BIBREF11"
                    },
                    {
                        "start": 1293,
                        "end": 1315,
                        "text": "Caliskan et al., 2017)",
                        "ref_id": "BIBREF14"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Positionality in Design and Evaluation.",
                "sec_num": "5.3"
            },
            {
                "text": "Discovery and disagreement are central components to the open research focus in qualitative HCI methods. When designing labeled datasets and shared tasks, NLP can adopt HCI's open approach; rather than constraining the data and labels to a test a single technical ability, decided a priori, NLP researchers can take an open approach-one that allows for complicated labels that emerge from multiple annotators' interpretation of the data. This opportunity to include labeler disagreements has been noted by a large body of work (Inie and Derczynski, 2021) , but given the complexity of narrative data and its many intertwining features (Piper et al., 2021) , customized labels (e.g., hierarchical) could more realistically represent narratives than artificial benchmarks with limited utility. On the other side, NLP methods like topic modeling can help surface themes and discourses that are not immediately apparent to qualitative coders (Baumer et al., 2017) . NLP methods can also be used to to identify outlier narratives whose structure or framing is unusual for the dataset (Antoniak et al., 2019) .",
                "cite_spans": [
                    {
                        "start": 527,
                        "end": 554,
                        "text": "(Inie and Derczynski, 2021)",
                        "ref_id": "BIBREF28"
                    },
                    {
                        "start": 635,
                        "end": 655,
                        "text": "(Piper et al., 2021)",
                        "ref_id": "BIBREF43"
                    },
                    {
                        "start": 938,
                        "end": 959,
                        "text": "(Baumer et al., 2017)",
                        "ref_id": "BIBREF8"
                    },
                    {
                        "start": 1079,
                        "end": 1102,
                        "text": "(Antoniak et al., 2019)",
                        "ref_id": "BIBREF2"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Openness to Discovery and Disagreement.",
                "sec_num": "5.4"
            },
            {
                "text": "As both a research tool and as an object of study, narrative datasets have been widely used in both NLP and HCI. This short work is not intended to describe all approaches to narrative in these disciplines, nor is it intended to provide solutions to all the described challenges. Boundaries between disciplines are fluid, especially in regards to stories shared on social media, where platform design, moderation, and many other HCI concepts have shaped the stories studied via computational NLP methods. Many different fields (e.g., literary studies) are concerned with narratives; we have constrained our discussion to datasets in NLP and qualitative HCI because we see room for crosspollination and conversations. Stories can be powerful tools of persuasion and expression, and richer methods that draw from both NLP and HCI can raise new questions and open up new directions.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusion",
                "sec_num": "6"
            }
        ],
        "back_matter": [
            {
                "text": "Thank you to our anonymous reviewers, whose comments were very helpful in preparing this paper. We also thank Sharifa Sultana's Facebook Fellowship for supporting this work.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgements",
                "sec_num": "7"
            }
        ],
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