A User Study on the Feasibility of Topic-aware Misinformation Warning on Social Media

Jingyi Xie, Michiharu Yamashita, Zekun Cai, Aiping Xiong

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Misinformation is one of the most fundamental problems in social media with increasing cases and underlying harmful effects on users. To mitigate such problem, misinformation warnings have been developed, including alerting with warning messages and hiding the contents. Previous studies mainly explored the most effective, one-size-fits-all design. Therefore, little has been known about customizable and flexible warning designs. In this study, we propose a “topic-aware misinformation warning” where users’ preferences for warning designs can vary on topics. To illustrate our ideas, we developed Twitter-like pages using three topics (i.e., politics, gossip, and Covid-19) and three designs (i.e., interstitial, contextual, and highlight). We conducted semi-structured interviews with 18 participants to explore their preferences and opinions on the designs. Our results show that users’ preferences for misinformation warnings are diverse in topics. Thus, topic-aware misinformation warning is promising to alleviate misinformation problems on Twitter.

Original languageEnglish (US)
Pages (from-to)621-625
Number of pages5
JournalProceedings of the Human Factors and Ergonomics Society
Volume66
Issue number1
DOIs
StatePublished - 2022
Event66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022 - Atlanta, United States
Duration: Oct 10 2022Oct 14 2022

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics

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