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1. NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges

   https://doi.org/10.1145/3479571  

   Bhuiyan, Md Momen; Horning, Michael; Lee, Sang Won; Mitra, Tanushree (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Struggling to curb misinformation, social media platforms are experimenting with design interventions to enhance consumption of credible news on their platforms. Some of these interventions, such as the use of warning messages, are examples of nudges---a choice-preserving technique to steer behavior. Despite their application, we do not know whether nudges could steer people into making conscious news credibility judgments online and if they do, under what constraints. To answer, we combine nudge techniques with heuristic based information processing to design NudgeCred--a browser extension for Twitter. NudgeCred directs users' attention to two design cues: authority of a source and other users' collective opinion on a report by activating three design nudges---Reliable, Questionable, and Unreliable, each denoting particular levels of credibility for news tweets. In a controlled experiment, we found that NudgeCred significantly helped users (n=430) distinguish news tweets' credibility, unrestricted by three behavioral confounds---political ideology, political cynicism, and media skepticism. A five-day field deployment with twelve participants revealed that NudgeCred improved their recognition of news items and attention towards all of our nudges, particularly towards Questionable. Among other considerations, participants proposed that designers should incorporate heuristics that users' would trust. Our work informs nudge-based system design approaches for online media. 

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2. Investigating “Who” in the Crowdsourcing of News Credibility

   Bhuiyan, Md Momen; Zhang, Amy; Sehat, Connie Moon; Mitra, Tanushree (March 2020, Computational Journalism C+J)

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   Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider 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. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources. 

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3. Investigating “Who” in the Crowdsourcing of News Credibility

   Bhuiyan, Momen; Zhang, Amy; Sehat, Connie; Mitra, Tanushree (January 2020, Computational Journalism Symposium)

   Concerns about the spread of misinformation online via news articles have led to the development of many tools and processes involving human annotation of their credibility. However, much is still unknown about how different people judge news credibility or the quality or reliability of news credibility ratings from populations of varying expertise. In this work, we consider 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. We find that both groups’ credibility ratings have higher correlation to journalism experts compared to the science experts, with 10-15 raters to achieve convergence. We also find that raters’ gender and political leaning impact their ratings. Among article genre of news/opinion/analysis and article source leaning of left/center/right, crowd ratings were more similar to experts respectively with opinion and strong left sources. 

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4. How Dependable are “First Impressions” to Distinguish between Real and Fake News Websites?

   https://doi.org/https://doi.org/10.1145/3342220.3343670  

   Huang, Dongchen; Zhu, Yige; Mustafaraj, Eni (September 2019, The 30th ACM International Conference on HyperText and Social Media (HT 2019))

   In an increasingly information-dense web, how do we ensure that we do not fall for unreliable information? To design better web literacy practices for assessing online information, we need to understand how people perceive the credibility of unfamiliar websites under time constraints. Would they be able to rate real news websites as more credible and fake news websites as less credible? We investigated this research question through an experimental study with 42 participants (mean age = 28.3) who were asked to rate the credibility of various “real news” (n = 14) and “fake news” (n = 14) websites under different time conditions (6s, 12s, 20s), and with a different advertising treatment (with or without ads). Participants did not visit the websites to make their credibility assessments; instead, they interacted with the images of website screen captures, which were modified to remove any mention of website names, to avoid the effect of name recognition. Participants rated the credibility of each website on a scale from 1 to 7 and in follow-up interviews provided justifications for their credibility scores. Through hypothesis testing, we find that participants, despite limited time exposure to each website (between 6 and 20 seconds), are quite good at the task of distinguishing between real and fake news websites, with real news websites being overall rated as more credible than fake news websites. Our results agree with the well-known theory of “first impressions” from psychology, that has established the human ability to infer character traits from faces. That is, participants can quickly infer meaningful visual and content cues from a website, that are helping them make the right credibility evaluation decision. 

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5. Misinformation Detection Algorithms and Fairness across Political Ideologies: The Impact of Article Level Labeling

   https://doi.org/10.1145/3578503.3583617  

   Park, Jinkyung; Ellezhuthil, Rahul Dev; Isaac, Joseph; Mergerson, Christoph; Feldman, Lauren; Singh, Vivek (April 2023, Proceedings of the 15th ACM Web Science Conference 2023)

   Multiple recent efforts have used large-scale data and computational models to automatically detect misinformation in online news articles. Given the potential impact of misinformation on democracy, many of these efforts have also used the political ideology of these articles to better model misinformation and study political bias in such algorithms. However, almost all such efforts have used source level labels for credibility and political alignment, thereby assigning the same credibility and political alignment label to all articles from the same source (e.g., the New York Times or Breitbart). Here, we report on the impact of journalistic best practices to label individual news articles for their credibility and political alignment. We found that while source level labels are decent proxies for political alignment labeling, they are very poor proxies-almost the same as flipping a coin-for credibility ratings. Next, we study the implications of such source level labeling on downstream processes such as the development of automated misinformation detection algorithms and political fairness audits therein. We find that the automated misinformation detection and fairness algorithms can be suitably revised to support their intended goals but might require different assumptions and methods than those which are appropriate using source level labeling. The results suggest caution in generalizing recent results on misinformation detection and political bias therein. On a positive note, this work shares a new dataset of journalistic quality individually labeled articles and an approach for misinformation detection and fairness audits. 

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