Iffy-Or-Not: Extending the Web to Support the Critical Evaluation of Fallacious Texts
Not on the map. Why?
Abstract (arXiv)
Social platforms have expanded opportunities for deliberation with the comments being used to inform one's opinion. However, using such information to form opinions is challenged by unsubstantiated or false content. To enhance the quality of opinion formation and potentially confer resistance to misinformation, we developed Iffy-Or-Not (ION), a browser extension that seeks to invoke critical thinking when reading texts. With three features guided by argumentation theory, ION highlights fallacious content, suggests diverse queries to probe them with, and offers deeper questions to consider and chat with others about. From a user study (N=18), we found that ION encourages users to be more attentive to the content, suggests queries that align with or are preferable to their own, and poses thought-provoking questions that expands their perspectives. However, some participants expressed aversion to ION due to misalignments with their information goals and thinking predispositions. Potential backfiring effects with ION are discussed.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 132 references that are in the map. First posted on arXiv 18 Mar 2025.
Builds on
22 of 132 references.
References · 22
- 2023A Survey on Multimodal Large Language Modelscited by 909
- 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 268
- 2020Metrics for Multi-Class Classification: an Overviewcited by 143
- 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 110
- 2020Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-makingcited by 59
- 2023The Perils & Promises of Fact-checking with Large Language Modelscited by 31
- 2021Dynamic Language Models for Continuously Evolving Contentcited by 27
- 2024Enhancing Critical Thinking in Education by means of a Socratic Chatbotcited by 21
- 2022Robust and Explainable Identification of Logical Fallacies in Natural Language Argumentscited by 12
- 2024Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Toolcited by 10
- 2024Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Researchcited by 10
- 2024Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation Platformscited by 9
10 more
- 2019Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?cited by 8
- 2021Do explanations increase the effectiveness of AI-crowd generated fake news warnings?cited by 8
- 2022Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformationcited by 8
- 2022Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubblescited by 8
- 2022Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussioncited by 7
- 2020Encounters with Visual Misinformation and Labels Across Platforms: An Interview and Diary Study to Inform Ecosystem Approaches to Misinformation Interventionscited by 6
- 2014Quantifying Information Overload in Social Media and its Impact on Social Contagionscited by 3
- 2019Selective Exposure shapes the Facebook News Dietcited by 3
- 2023ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Readingcited by 3
- 2020Nudge for Deliberativeness: How Interface Features Influence Online Discoursecited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.