Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2021
Abstract
An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” to a relevant claim for specified events with political stances. Finally, we proposed two ways showing the echo chamber effects: 1) visualizing the event and the associated pieces of news; and 2) visualizing the stance distribution of news from news sources of different political ideology. By making the echo chamber effect explicit, we expect online users will become exposed to more diverse perspectives toward a specific event. The demo video of our platform is available on Youtube.
Keywords
Echo Chamber, News Stance, Social Media Bias, Web Application
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Research Areas
Data Science and Engineering
Publication
WWW '21: Companion Proceedings of the Web Conference: Ljubljana, Slovenia, April 19-23
First Page
713
Last Page
716
ISBN
9781450383134
Identifier
10.1145/3442442.3458613
Publisher
ACM
City or Country
New York
Citation
LO, Kuan-Chieh; DAI, Shih-Chieh; XIONG, Aiping; JIANG, Jing; and KU, Lun-Wei.
Escape from an echo chamber. (2021). WWW '21: Companion Proceedings of the Web Conference: Ljubljana, Slovenia, April 19-23. 713-716.
Available at: https://ink.library.smu.edu.sg/sis_research/6949
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3442442.3458613
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons