TY - GEN
T1 - A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities
AU - Wang, Yuping
AU - Zannettou, Savvas
AU - Blackburn, Jeremy
AU - Bradlyn, Barry
AU - De Cristofaro, Emiliano
AU - Stringhini, Gianluca
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The news ecosystem encompasses a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they are discussed, and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w.r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.
AB - The news ecosystem encompasses a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they are discussed, and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w.r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.
UR - http://www.scopus.com/inward/record.url?scp=85125300114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125300114&partnerID=8YFLogxK
U2 - 10.1109/BigData52589.2021.9671843
DO - 10.1109/BigData52589.2021.9671843
M3 - Conference contribution
AN - SCOPUS:85125300114
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 1481
EP - 1492
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
Y2 - 15 December 2021 through 18 December 2021
ER -