NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
Md Momen Bhuiyan, Michael A. Horning, Sang Won Lee, Tanushree Mitra
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers15
- See Widely, Think Wisely: Toward Designing a Generative Multi-agent System to Burst Filter BubblesYu Zhang, Jingwei Sun, Li Feng, Cen Yao et al.CHI 2024 · 38 citations
- "The Headline Was So Wild That I Had To Check": An Exploration of Women's Encounters With Health Misinformation on Social MediaLisa Mekioussa Malki, Dilisha Patel, Aneesha SinghCSCW 2024 · 26 citations
- A Browser Extension for in-place Signaling and Assessment of MisinformationFarnaz Jahanbakhsh, David R. KargerCHI 2024 · 24 citations
- How Do HCI Researchers Study Cognitive Biases? A Scoping ReviewNattapat Boonprakong, Benjamin Tag, Jorge Gonçalves, Tilman DinglerCHI 2025 · 19 citations
- Viblio: Introducing Credibility Signals and Citations to Video-Sharing PlatformsEmelia May Hughes, Renee Wang, Prerna Juneja, Tony W. Li et al.CHI 2024 · 12 citations
Builds on4
- Fake News on Facebook and Twitter: Investigating How People (Don't) InvestigateChristine Geeng, Savanna Yee, Franziska RoesnerCHI 2020 · 154 citations
- Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert CriteriaMd Momen Bhuiyan, Amy X. Zhang, Connie Moon Sehat, Tanushree MitraCSCW 2020 · 70 citations
- Synthesized Social Signals: Computationally-Derived Social Signals from Account HistoriesJane Im, Sonali Tandon, Eshwar Chandrasekharan, Taylor Denby et al.CHI 2020 · 38 citations
- Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It ImminentlyGeza Kovacs, Zhengxuan Wu, Michael S. BernsteinCHI 2021 · 21 citations
Related papers
- Tweet Trajectory and AMPS-based Contextual Cues can Help Users Identify MisinformationHimanshu Zade, Megan Woodruff, Erika Johnson, Mariah Stanley et al.CSCW 2023 · 10 citations
- Our Browser Extension Lets Readers Change the Headlines on News Articles, and You Won't Believe What They Did!Farnaz Jahanbakhsh, Amy X. Zhang, Karrie Karahalios, David R. KargerCSCW 2022 · 9 citations
- The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social InfluenceZhuoran Lu, Patrick Li, Weilong Wang, Ming YinCSCW 2022 · 55 citations
- Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data ExplorersMaxim Lisnic, Zach Cutler, Marina Kogan, Alexander LexCHI 2025 · 9 citations
- Will the Crowd Game the Algorithm?: Using Layperson Judgments to Combat Misinformation on Social Media by Downranking Distrusted SourcesZiv Epstein, Gordon Pennycook, David G. RandCHI 2020 · 68 citations
