FeedReflect

Bhuiyan, Momen; Zhang, Kexin; Vick, Kelsey; Horning, Michael A.; Mitra, Tanushree

doi:10.1145/3272973.3274056

Cited by 40 publications

(12 citation statements)

References 15 publications

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“…Outside the classroom setting, there have also been attempts to design systems that support people in reflective thinking about online content (Bhuiyan et al, 2018;Gupta et al, 2014;Chen et al, 2015). Chen et al (2015) introduce a methodology to help users detect clickbait that is disguised as online news.…”

Section: Supporting Reflective Assessmentmentioning

confidence: 99%

“…Chen et al (2015) introduce a methodology to help users detect clickbait that is disguised as online news. Tools have also been developed to help people reflect on the credibility of tweets, i.e., TweetCred (Gupta et al, 2014) and FeedReflect (Bhuiyan et al, 2018). For example, FeedReflect (Bhuiyan et al, 2018) uses visual cues, or nudges, in the form of tooltips, to indicate credibility, as well as questions to encourage users to reflect on tweets credibility.…”

Section: Supporting Reflective Assessmentmentioning

confidence: 99%

“…Tools have also been developed to help people reflect on the credibility of tweets, i.e., TweetCred (Gupta et al, 2014) and FeedReflect (Bhuiyan et al, 2018). For example, FeedReflect (Bhuiyan et al, 2018) uses visual cues, or nudges, in the form of tooltips, to indicate credibility, as well as questions to encourage users to reflect on tweets credibility. The EVON tool (Hahnel et al, 2020), developed to understand how university students evaluate online information provided by a search engine, proved useful to support users in selfreflection.…”

Section: Supporting Reflective Assessmentmentioning

confidence: 99%

“…Studies have pointed out difficulties users have in critical evaluation and use of online information (Hahnel et al, 2020;Fraillon et al, 2020), as well as in identifying reliable and trustworthy information and sources (Walraven et al, 2008). Extensive research has focused on assessing the credibility of tweets (Bhuiyan et al, 2018;Castillo et al, 2011), news (Chen et al, 2015;Popat et al, 2016), or web blogs (Jo et al, 2019); however, videos are less frequently researched. Research has also shown that trying to influence users' beliefs might strengthen their position rather than encourage reflection (Lewandowsky et al, 2012;Nguyen et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…One solution is to simply provide additional information (Bhuiyan et al, 2018;Kahneman, 2011) to help people reason actively about the videos they consume online, instead of encouraging them to change their opinion. We consider controversial topics, such as, vaccination, Catalonia independence, and free trade suitable for studying the role of reflective thinking.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

Inel¹,

Đuričić²,

Kaur³

et al. 2021

Front. Artif. Intell.

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Online videos have become a prevalent means for people to acquire information. Videos, however, are often polarized, misleading, or contain topics on which people have different, contradictory views. In this work, we introduce natural language explanations to stimulate more deliberate reasoning about videos and raise users’ awareness of potentially deceiving or biased information. With these explanations, we aim to support users in actively deciding and reflecting on the usefulness of the videos. We generate the explanations through an end-to-end pipeline that extracts reflection triggers so users receive additional information to the video based on its source, covered topics, communicated emotions, and sentiment. In a between-subjects user study, we examine the effect of showing the explanations for videos on three controversial topics. Besides, we assess the users’ alignment with the video’s message and how strong their belief is about the topic. Our results indicate that respondents’ alignment with the video’s message is critical to evaluate the video’s usefulness. Overall, the explanations were found to be useful and of high quality. While the explanations do not influence the perceived usefulness of the videos compared to only seeing the video, people with an extreme negative alignment with a video’s message perceived it as less useful (with or without explanations) and felt more confident in their assessment. We relate our findings to cognitive dissonance since users seem to be less receptive to explanations when the video’s message strongly challenges their beliefs. Given these findings, we provide a set of design implications for explanations grounded in theories on reducing cognitive dissonance in light of raising awareness about online deception.

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Section: Supporting Reflective Assessmentmentioning

confidence: 99%

Section: Supporting Reflective Assessmentmentioning

confidence: 99%

Section: Supporting Reflective Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

Inel¹,

Đuričić²,

Kaur³

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

Exploring the impact of automated correction of misinformation in social media

Burel,

Tavakoli,

Alani

2024

AI Magazine

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Correcting misinformation is a complex task, influenced by various psychological, social, and technical factors. Most research evaluation methods for identifying effective correction approaches tend to rely on either crowdsourcing, questionnaires, lab‐based simulations, or hypothetical scenarios. However, the translation of these methods and findings into real‐world settings, where individuals willingly and freely disseminate misinformation, remains largely unexplored. Consequently, we lack a comprehensive understanding of how individuals who share misinformation in natural online environments would respond to corrective interventions. In this study, we explore the effectiveness of corrective messaging on 3898 users who shared misinformation on Twitter/X over 2 years. We designed and deployed a bot to automatically identify individuals who share misinformation and subsequently alert them to related fact‐checks in various message formats. Our analysis shows that only a small minority of users react positively to the corrective messages, with most users either ignoring them or reacting negatively. Nevertheless, we also found that more active users were proportionally more likely to react positively to corrections and we observed that different message tones made particular user groups more likely to react to the bot.

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Navigating misinformation in voice messages: Identification of user‐centered features for digital interventions

Hartwig,

Sandler,

Reuter

2024

Risk Hazard & Crisis Pub Pol

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Misinformation presents a challenge to democracies, particularly in times of crisis. One way in which misinformation is spread is through voice messages sent via messenger groups, which enable members to share information on a larger scale. Gaining user perspectives on digital misinformation interventions as countermeasure after detection is crucial. In this paper, we extract potential features of misinformation in voice messages from literature, implement them within a program that automatically processes voice messages, and evaluate their perceived usefulness and comprehensibility as user‐centered indicators. We propose 35 features extracted from audio files at the character, word, sentence, audio, and creator levels to assist (1) private individuals in conducting credibility assessments, (2) government agencies faced with data overload during crises, and (3) researchers seeking to gather features for automatic detection approaches. We conducted a think‐aloud study with laypersons () to provide initial insight into how individuals autonomously assess the credibility of voice messages, as well as which automatically extracted features they find to be clear and convincing indicators of misinformation. Our study provides qualitative and quantitative insights into valuable indicators, particularly when they relate directly to the content or its creator, and uncovers challenges in user interface design.

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FeedReflect

Cited by 40 publications

References 15 publications

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

Exploring the impact of automated correction of misinformation in social media

Navigating misinformation in voice messages: Identification of user‐centered features for digital interventions

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