CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment
Yang Ouyang, Shenghan Gao, Ruichuan Wang, Hailiang Zhu, Yuheng Shao, Xiaoyu Gu, and Quan Li

TL;DR
CommSense is a tool designed to improve online comment navigation by reducing biases and promoting reflective reasoning, thereby helping users form more rational judgments through interface design and real-time prompts.
Contribution
This paper introduces CommSense, a novel plugin that enhances comment engagement with visual summaries and prompts, addressing bias mitigation and reflective reasoning in online discussions.
Findings
CommSense increases bias awareness among users.
The tool promotes more comprehensive, evidence-based reasoning.
Users report high usability and improved reflection.
Abstract
Online comments significantly influence users' judgments, yet their presentation, often determined by platform algorithms, can introduce biases, such as anchoring effects, which distort reasoning. While existing research emphasizes mitigating individual cognitive biases, the evolution of user judgments during comment engagement remains overlooked. This study investigates how presentation cues impact reasoning and explores interface design strategies to mitigate bias. Through a preliminary experiment (N=18) and a co-design workshop, we identified key challenges users face across a four-stage process and distilled four design requirements: pre-engagement framing, interactive organization, reflective prompts, and synthesis support. Based on these insights, we developed CommSense, an on-the-fly plugin that enhances user engagement with online comments by providing visual overviews and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInnovative Human-Technology Interaction · AI in Service Interactions · Expert finding and Q&A systems
