• Characterizing Political Campaigning with Lexical Mutants on Indian Social Media
    S. Phadke, T. Mitra  |  ICWSM 2024  |  paper
    Using multilingual embeddings and network analysis, we detect over 3.8K political campaigns with text mutations spanning multiple languages and social media platforms in India. Our work contributes insights into how lexical mutations can be leveraged to bypass the platform manipulation policies and how such competing campaigning can provide an exaggerated sense of political divide on Indian social media.


  • Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube
    P. Juneja, M. Bhuiyan, T. Mitra  |  CHI 2023  |  paper doi 
    We conduct a 9-day crowd-sourced audit on YouTube to assess the extent of enactment of policies introduced by YouTube to remove conspiratorial claims about the 2020 presidential elections. We find that YouTube's search results, irrespective of search query bias, contain more videos that oppose rather than support election misinformation.
  • NewsComp: Facilitating Diverse News Reading through Comparative Annotation
    M. Bhuiyan, S. Lee, N. Goyal, T. Mitra  |  CHI 2023  |  paper doi 
    We examine how comparative annotation affects users' perceptions of article credibility and quality. We found that the comparison process led users to notice differences in information placement/depth, degree of factuality/opinion, and empathetic/inflammatory language use establishing that comparative annotation can marginally impact users' credibility perceptions in certain cases.
  • Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations
    B. Keith, T. Mitra, C. North  |  IUI 2023  |  paper doi 
    We present a semantic interaction (SI) framework for narrative maps that can support analysts through their sensemaking process. In contrast to traditional SI systems, our approach has an additional abstraction layer—the structure space—that builds upon the projection space and encodes the narrative in a discrete structure. We find that our SI system can model the analysts’ intent and support incremental formalism for narrative maps.
  • A Survey on Event-based News Narrative Extraction
    B. Keith, T. Mitra, C. North  |  ACM Computing Surveys 2023  |  paper doi 
    This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.


  • Human and Technological Infrastructures of Fact-checking
    P. Juneja, T. Mitra  |  CSCW 2022  |  paper doi 
    This paper unpacks the fact-checking process by revealing the infrastructures—both human and technological—that support and shape fact-checking work.O ur findings highlight that the fact-checking process is a collaborative effort among various stakeholder groups and associated technological and informational infrastructures.
  • Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions
    S. Phadke, M. Samory, T. Mitra  |  ICWSM 2022  |  paper doi  | Best Paper Award
    What are the pathways of online conspiracy engagement? How does conspiracy radicalization process evolve for users? Through a theory-driven, empirical study of the conspiracy radicalization process, we answer these questions by studying 36K Reddit users through their 169M contributions.
  • OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
    M. Bhuiyan, C. Isaza, S. Lee, T. Mitra  |  CHI 2022  |  paper doi 
    We investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube, a browser extension for YouTube that displays strangers' personalized YouTube recommendations.
  • Characterizing Social Movement Narratives in Online Communities: The 2021 Cuban Protests on Reddit
    B. Keith, T. Mitra, C. North  |  Computational Journalism 2022  |  paper doi 
    How can we model community acceptance and extract the accepted narratives of a social media community? In this work, we propose a narrative extraction algorithm that incorporates the concept of community acceptance to model and extract the accepted narratives from social media communities.
  • Design Guidelines for Narrative Maps in Sensemaking Tasks
    B. Keith, T. Mitra, C. North  |  Journal of Information Visualization 2022  |  paper doi 
    We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking.


  • Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities
    S. Phadke, M. Samory, T. Mitra  |  CSCW 2021  |  paper doi  | Best Paper Honorable Mention
    We characterize self-disclosures of dissonance about QAnon in conspiracy subreddits and uncover the symbolic language representing the movement, expectations, practices, heroes and foes of the QAnon community. We offer a computational framework for distinguishing belief and dissonance from general discussion about QAnon.
  • Educators, Solicitors, Flamers, Motivators, Sympathizers: Characterizing Roles in Online Extremist Movements
    S. Phadke, T. Mitra  |  CSCW 2021  |  paper doi 
    We investigate online participatory activism in extremist movements on Facebook. We characterize their social roles, role dynamics, and influence. We find that the role core to the extremist movements—educators and solicitors—are most influential.
  • NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
    M. Bhuiyan, M. Horning, S. Lee, T. Mitra  |  CSCW 2021  |  paper doi 
    We combine nudge techniques with heuristic based information processing to design NudgeCred–a browser extension for Twitter. We find NudgeCred significantly helped users distinguish news tweets' credibility, unrestricted by three behavioral confounds—political ideology, political cynicism, and media skepticism.
  • Designing Transparency Cues in Online News Platforms to Promote Trust: Journalists’ & Consumers’ Perspectives
    M. Bhuiyan, H. Whitley, M. Horning, S. Lee, T. Mitra  |  CSCW 2021  |  paper doi 
    How can we utilize transparency to promote trust in news? We answer by interviewing journalists and news consumers—the two stakeholders in a news system. We find new design considerations for building trustworthy news platforms, such as designing for easy comprehension, presenting appropriate details in news articles, and comparing attributes across news organizations to highlight diverging practices.
  • Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
    P. Juneja, T. Mitra  |  CHI 2021  |  paper doi  |  datasetBest Paper Honorable Mention
    press: The Seattle Times, VentureBeat
    We audit for vaccine misinformation on the search and recommendation algorithms of the world’s leading e-retailer-Amazon. We find 10.47% of search-results promote misinformative health products with evidence of filter-bubble effect in their recommendations; accounts performing actions on misinformative products are presented with more misinformation.
  • Narrative Sensemaking: Strategies for Narrative Maps Construction
    B. Keith, T. Mitra, C. North  |  IEEE Viz 2021  |  paper doi 
    Narrative sensemaking is a fundamental process to understand sequential information. We focus on a specific type of graph-based narrative representation—narrative maps and seek to understand how analysts construct narrative maps to design better narrative representation and extraction methods.


  • What Makes People Join Conspiracy Communities?: Role of Social Factors in Conspiracy Engagement
    S. Phadke, M. Samory, T. Mitra  |  CSCW 2020  |  paper doi  | Best Paper Honorable Mention
    press: NewScientist
    What makes users join communities endorsing and spreading conspiracy theories? Leveraging longitudinal data from 56 conspiracy communities on Reddit comprising 6M posts and comments, we find that dyadic interactions with members of the conspiracy communities and marginalization outside of the conspiracy communities, are the most important social precursors to conspiracy joining.
  • Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube
    E. Hussein, P. Juneja, T. Mitra  |  CSCW 2020  |  paper doi  |  dataset
    press: Business Insider, The Hill
    We audit YouTube to investigate whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying misinformation. While demographics do not have a significant effect, once a user develops a watch history, it affects the extent of misinformation recommended to them.
  • Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
    B. Keith, T. Mitra  |  CSCW 2020  |  paper doi 
    Narratives are fundamental to our perception of the world. By combining the theory of narrative representations with the data from modern online systems, we make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach.
  • Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria
    M. Bhuiyan, A. Zhang, C. Sehat, T. Mitra  |  CSCW 2020  |  paper doi  |  dataset
    We investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ. We find differences in performance due to the makeup of the crowd, such as rater demographics and political leaning, as well as the scope of the tasks that the crowd is assigned to rate, such as the genre of the article and partisanship of the publication.
  • Many Faced Hate: A Cross Platform Study of Content Framing and Information Sharing by Online Hate Groups
    S. Phadke, T. Mitra  |  CHI 2020  |  paper doi 
    How do hate groups frame their agenda and share information across social media platforms? Our study unravels the ecosystem of cross-platform communication by 72 hate groups, suggesting that they use Facebook for group radicalization and recruitment, while Twitter for reaching a diverse follower base.
  • Characterizing the Social Media News Sphere through User Co-Sharing Practices
    S. Samory, V.Abnousi, T. Mitra  |  ICWSM 2020  |  paper doi 
    What communities of news sources emerge, when considering the sharing practices of their social media audience? We answer by quantitatively analyzing over 31 million tweets and 1 million news articles from 639 news sources, both credible and questionable.
  • Evaluating the Inverted Pyramid Structure through Automatic 5W1H Extraction and Summarization
    B. Keith, T. Mitra  |  C+J 2020  |  paper doi 
    The inverted pyramid structure is a cornerstone of journalism associated with neutrality and objectivity. Can we determine how well an article follows this structure? We propose a quantitative approach using summarization and 5W1H extraction to do this in an Associated Press news articles data set.
  • Investigating "Who" in the Crowdsourcing of News Credibility
    M. Bhuiyan, A. Zhang, C. Sehat, T. Mitra  |  C+J 2020  |  paper doi 
    How do different people judge the credibility of news? We answer by considering credibility ratings from two ``crowd' populations: 1) students within journalism or media programs, and 2) crowd workers on UpWork, and compare them with the ratings of two sets of experts: journalists and climate scientists, on a set of 50 climate-science articles.
  • Through the Looking Glass: Study of Transparency in Reddit’s Moderation Practices
    P. Juneja, D. Ramasubramanian, T. Mitra  |  GROUP 2020  |  paper doi 
    How do content moderation practices in Reddit sub-communities align with principles of transparency? By employing a mixed-methods approach of analyzing public moderation logs, we find a lack of transparency in moderation practices. Interviewing Reddit moderators reveal that they are divided in their stance on transparency.


  • SENPAI: Supporting Exploratory Text Analysis through Semantic & Syntactic Pattern Inspection
    M. Samory, T. Mitra  |  ICWSM 2019  |  paper doi 
    Analyzing language for social computing tasks requires looking beyond individual words. What are the relevant patterns for a task, and how to find them? We introduce SENPAI, a novel tool that discovers combined semantic and syntactic patterns.


  • The Government Spies Using Our Webcams: The Language of Conspiracy Theories in Online Discussions
    M. Samory, T. Mitra  |  CSCW 2018  |  paper doi 
    What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do they tell us about the way users think? By focusing on three key elements - the conspiratorial agent, their actions, and their targets, this work answers the above questions.
  • FeedReflect: A toolkit for engaging users in active reflection on Twitter
    M. Bhuiyan, K. Zhang, K. Vick, M. Horning, & T. Mitra  |  Extended Abstract of CSCW 2018  |  paper doi 
    We introduced a system to engage users in careful evaluation of news credibility on Twitter.
  • Framing Hate with Hate Frames: Designing the Codebook
    S. Phadke, J. Lloyd, J. Hawdon, & T. Mitra  |  Extended Abstract of CSCW 2018  |  paper doi 
    By using Snow and Benfords framing theory we establish a coding scheme for analyzing characteristics of hate-group communications online.
  • Conspiracies Online: User discussions in a Conspiracy Community Following Dramatic Events
    M. Samory, T. Mitra  |  ICWSM 2018  |  paper doi 
    press: Wired
    By focusing on four tragic events and 10 years of discussions in a popular online conspiracy theory community on Reddit, we study the evolution of users’ tenures in the community and the effects of the events on their discusion dynamics.
  • Credibility and the Dynamics of Collective Attention
    T. Mitra, G. Wright & E. Gilbert  |  CSCW 2018 Online First  |  paper doi 
    Representing collective attention by the aggregate temporal signatures of an event’s reportage, we found that the amount of continued attention focused on an event provides information about its associated levels of perceived credibility.
  • Spread of Employee Engagement in a Large Organizational Network: A Longitudinal Analysis
    T. Mitra, M. Muller, N. Shami, A Golestani & M. Masli  |  CSCW 2018 Online First  |  paper doi 
    Using employees' organizational social media data and their workplace hierarchical network structure, we studied contagion across a large multinational corporation, focusing on an important workplace behavior – employee engagement.
  • Growth in Social Network Connectedness among Different Roles in Organizational Crowdfunding
    Michael Muller, T. Mitra, Werner Geyer  |  GROUP 2018  |  paper doi 
    In a large-scale organizational crowdfunding campaign, we found that people in different crowdfunding roles experience different degrees of growth in their social networks, during and after the campaign.


  • A Parsimonious Language Model of Social Media Credibility Across Disparate Events
    T. Mitra, G. Wright & E. Gilbert  |  CSCW 2017  |  paper doi 
    We present a parsimonious model that maps language cues to perceived levels of credibility. Our results show that certain linguistic categories and their associated phrases are strong predictors surrounding disparate social media events. For example, hedge words and positive emotion words are associated with lower credibility.


  • Understanding Anti-Vaccination Attitudes in Social Media
    T. Mitra, S. Counts & J. W. Pennebaker  |  ICWSM 2016  |  paper doi 
    What drives people to develop and perpetuate the anti-vaccination movement? Our results show that those with long-term anti-vaccination attitudes manifest conspiratorial thinking and mistrust in government.
  • Recovery Amid Pro-Anorexia: Analysis of Recovery in Social Media
    S. Chancellor & T. Mitra & M. Choudhury  |  CHI 2016  |  paper doi 
    By developing a statistical framework using survival analysis, we find that recovery on Tumblr is protracted. Only half of the population shows likelihood of recovery after four years, and a vast minority is not estimated to recover even at the end of six years.


  • CREDBANK: A Large-scale Social Media Corpus With Associated Credibility Annotations
    T. Mitra & E. Gilbert  |  ICWSM 2015  |  paper doi  |  dataset
    In this paper we present CREDBANK, a corpus of tweets, topics, events and associated human credibility judgements based on the real-time tracking of events on Twitter.
  • Comparing Person-and Process-centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk
    T. Mitra, C.J. Hutto & E. Gilbert  |  CHI 2015  |  paper doi  | Best Paper Honorable Mention
    We measure the efficacy of selected strategies for obtaining high quality data annotations from non-experts. Our results point to the advantages of person-oriented strategies over process-oriented strategies.


  • Modeling Factuality Judgments in Social Media Text
    S. Soni, T. Mitra, E. Gilbert & J. Eisenstein  |  ACL 2014  |  paper doi 
    As events unfold, journalists and political commentators use quotes — often indirect — to convey potentially uncertain information and claims from their sources and informants. By obtaining annotations of perceived certainty of quoted statements in Twitter and comparing the ability of linguistic and extra-linguistic features to predict readers’ assessment of the certainty, we find that readers are influenced by linguistic framing devices and do not consider other factors, e.g. sources, journalist.
  • The Language that Gets People to Give: Phrases that Predict Success on Kickstarter
    T. Mitra & E. Gilbert  |  CSCW 2014  |  paper doi 
    We explore the factors which lead to funding on Kickstarter. Applying natural language methods and statistical analysis techniques to a corpus of crowdfunded projects, we find that the language used in the project has surprising predictive power–accounting for 58.56% of the variance around successful funding. A closer look at the phrases shows they exhibit general persuasion principles.


  • Analyzing Gossip in Workplace Email
    T. Mitra & E. Gilbert  |  ACM Newsletter Winter 2013  |  paper doi 
    Adopting the Enron email dataset and natural language techniques, we find that workplace gossip is common at all levels of the organizational hierarchy, with people most likely to gossip with their peers
  • Mechanical Turk is Not Anonymous.
    M. Lease, J. Hullman, J.P. Bigham, M.S. Bernstein, J. Kim, W.S. Lasecki, S. Bakhshi, T. Mitra & R.C. Miller.  |  Social Science Research Network 2013  |  paper doi 
    Adopting the Enron email dataset and natural language techniques, we find that workplace gossip is common at all levels of the organizational hierarchy, with people most likely to gossip with their peers


  • Have You Heard?: How Gossip Flows Through Workplace Email
    T. Mitra & E. Gilbert  |  ICWSM 2012  |  paper doi 
    Gossip is fundamental to social life. Here, we present the first large-scale study of gossip in CMC, looking at email where someone is mentioned in the message body but not included on the recipient list. We find that gossip emails are often more negative and people have a greater likelihood of sending gossip messages to smaller audiences.
  • Cost, Precision, and Task Structure in Aggression-Based Arbitration for Minimalist Robot Cooperation
    T. Mitra & D. A. Shell  |  SAB 2012  |  paper doi