TY - GEN
T1 - Understanding engagement with U.S. (mis)information news sources on Facebook
AU - Edelson, Laura
AU - Nguyen, Minh Kha
AU - Goldstein, Ian
AU - Goga, Oana
AU - McCoy, Damon
AU - Lauinger, Tobias
N1 - Funding Information:
We wish to thank the employees of CrowdTangle and Facebook who built the tools to enable our analysis. Particular thanks are owed to Naomi Shiffman for her help working with CrowdTangle and her insightful feedback on drafts of this work. Cybersecurity for Democracy at NYU’s Center for Cybersecurity has been supported by Democracy Fund, Luminate, Media Democracy Fund, the National Science Foundation under grant 1814816, Reset, and Well-spring. This research was supported in part by the French National Research Agency (ANR) through the ANR-17-CE23-0014 and the MIAI@Grenoble Alpes ANR-19-P3IA-0003 grants and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101021377. This material is based upon work supported by the Google Cloud Research Credits program.
Publisher Copyright:
© 2021 ACM.
PY - 2021/11/2
Y1 - 2021/11/2
N2 - Facebook has become an important platform for news publishers to promote their work and engage with their readers. Some news pages on Facebook have a reputation for consistently low factualness in their reporting, and there is concern that Facebook allows their misinformation to reach large audiences. To date, there is remarkably little empirical data about how often users "like," comment and share content from news pages on Facebook, how user engagement compares between sources that have a reputation for misinformation and those that do not, and how the political leaning of the source impacts the equation. In this work, we propose a methodology to generate a list of news publishers' official Facebook pages annotated with their partisanship and (mis)information status based on third-party evaluations, and collect engagement data for the 7.5 M posts that 2,551 U.S. news publishers made on their pages during the 2020 U.S. presidential election. We propose three metrics to study engagement (1) across the Facebook news ecosystem, (2) between (mis)information providers and their audiences, and (3) with individual pieces of content from (mis)information providers. Our results show that misinformation news sources receive widespread engagement on Facebook, accounting for 68.1% of all engagement with far-right news providers, followed by 37.7 % on the far left. Individual posts from misinformation news providers receive consistently higher median engagement than non-misinformation in every partisanship group. While most prevalent on the far right, misinformation appears to be an issue across the political spectrum.
AB - Facebook has become an important platform for news publishers to promote their work and engage with their readers. Some news pages on Facebook have a reputation for consistently low factualness in their reporting, and there is concern that Facebook allows their misinformation to reach large audiences. To date, there is remarkably little empirical data about how often users "like," comment and share content from news pages on Facebook, how user engagement compares between sources that have a reputation for misinformation and those that do not, and how the political leaning of the source impacts the equation. In this work, we propose a methodology to generate a list of news publishers' official Facebook pages annotated with their partisanship and (mis)information status based on third-party evaluations, and collect engagement data for the 7.5 M posts that 2,551 U.S. news publishers made on their pages during the 2020 U.S. presidential election. We propose three metrics to study engagement (1) across the Facebook news ecosystem, (2) between (mis)information providers and their audiences, and (3) with individual pieces of content from (mis)information providers. Our results show that misinformation news sources receive widespread engagement on Facebook, accounting for 68.1% of all engagement with far-right news providers, followed by 37.7 % on the far left. Individual posts from misinformation news providers receive consistently higher median engagement than non-misinformation in every partisanship group. While most prevalent on the far right, misinformation appears to be an issue across the political spectrum.
KW - Facebook
KW - engagement
KW - measurement
KW - misinformation
KW - news
UR - http://www.scopus.com/inward/record.url?scp=85118981288&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118981288&partnerID=8YFLogxK
U2 - 10.1145/3487552.3487859
DO - 10.1145/3487552.3487859
M3 - Conference contribution
AN - SCOPUS:85118981288
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 444
EP - 463
BT - IMC 2021 - Proceedings of the 2021 ACM Internet Measurement Conference
PB - Association for Computing Machinery
T2 - 21st ACM Internet Measurement Conference, IMC 2021
Y2 - 2 November 2021 through 4 November 2021
ER -