Tanushree Mitra

Publications

2020

• Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube
  E. Hussein, P. Juneja, T. Mitra  |  CSCW 2020  |  paper   |  doi  |  dataset
  

  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  
  

  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.

• What Makes People Join Conspiracy Communities?: Role of Social Factors in Conspiracy Engagement
  S. Phadke, M. Samory, T. Mitra  |  CSCW 2020  |  paper  
  

  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.

• Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteria
  M. Bhuiyan, A. Zhang, C. Sehat, T. Mitra  |  CSCW 2020  |  paper   |  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  
  

  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.

• 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.

2019

• 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.

2018

• 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 
  

  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.

2017

• 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.

2016

• 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.

2015

• 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 
  

  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.

2014

• 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.

2013

• 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

2012

• 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