# Iffy-Or-Not: Extending the Web to Support the Critical Evaluation of Fallacious Texts

arXiv: https://arxiv.org/abs/2503.14412 · HTML: https://mlsanity.com/paper/2503.14412/

## Key facts

- Cited by 1 papers 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 (most cited first, 22 of 22)

- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [The Perils & Promises of Fact-checking with Large Language Models](https://mlsanity.com/paper/2310.13549/) (2023)
- [Dynamic Language Models for Continuously Evolving Content](https://mlsanity.com/paper/2106.06297/) (2021)
- [Enhancing Critical Thinking in Education by means of a Socratic Chatbot](https://mlsanity.com/paper/2409.05511/) (2024)
- [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](https://mlsanity.com/paper/2212.07425/) (2022)
- [Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool](https://mlsanity.com/paper/2402.19135/) (2024)
- [Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research](https://mlsanity.com/paper/2404.05213/) (2024)
- [Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation Platforms](https://mlsanity.com/paper/2401.10820/) (2024)
- [Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?](https://mlsanity.com/paper/1904.01531/) (2019)
- [Do explanations increase the effectiveness of AI-crowd generated fake news warnings?](https://mlsanity.com/paper/2112.03450/) (2021)
- [Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation](https://mlsanity.com/paper/2203.00710/) (2022)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion](https://mlsanity.com/paper/2204.07741/) (2022)
- [Encounters with Visual Misinformation and Labels Across Platforms: An Interview and Diary Study to Inform Ecosystem Approaches to Misinformation Interventions](https://mlsanity.com/paper/2011.12758/) (2020)
- [Quantifying Information Overload in Social Media and its Impact on Social Contagions](https://mlsanity.com/paper/1403.6838/) (2014)
- [Selective Exposure shapes the Facebook News Diet](https://mlsanity.com/paper/1903.00699/) (2019)
- [ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading](https://mlsanity.com/paper/2306.07875/) (2023)
- [Nudge for Deliberativeness: How Interface Features Influence Online Discourse](https://mlsanity.com/paper/2001.04612/) (2020)

## Built on by (most cited first, 1 of 1)

- [CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments](https://mlsanity.com/paper/2604.27905/) (2026)

## 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.