Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior

Luca, Luceri; Ashok, Deb; Adam, Badawy; Emilio, Ferrara (17 May 2019). Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior. In: WWW ’19 Companion. WWW '19 Companion Proceedings of The 2019 World Wide Web Conference (pp. 1007-1012). New York: ACM 10.1145/3308560.3316735

[img]
Preview
Text
Red_Bots_do_it_Better.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (2MB) | Preview

Recent research brought awareness of the issue of bots on socialmedia and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US midterm elections and analyze social bot activity and interactions with humans. We collected 2.6 million tweets for 42 days around the election day from nearly 1 million users. We use the collected tweets to answer three research questions: (i) Do social bots lean and behave according to a political ideology? (ii) Can we observe different strategies among liberal and conservative bots? (iii )How effective are bot strategies in engaging humans? We show that social bots can be accurately classified according to their political leaning and behave accordingly. Conservative bots share most of the topics of discussion with their human counter-parts, while liberal bots show less overlap and a more inflammatory attitude. We studied bot interactions with humans and observed different strategies. Finally, we measured bots embeddedness in the social network and the extent of human engagement with each group of bots. Results show that conservative bots are more deeply embedded in the social network and more effective than liberal bots at exerting influence on humans.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)
08 Faculty of Science > Institute of Computer Science (INF)

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISBN:

978-1-4503-6675-5

Series:

WWW '19 Companion Proceedings of The 2019 World Wide Web Conference

Publisher:

ACM

Language:

English

Submitter:

Eirini Kalogeiton

Date Deposited:

19 Jul 2019 16:56

Last Modified:

24 Oct 2019 12:30

Publisher DOI:

10.1145/3308560.3316735

BORIS DOI:

10.7892/boris.131225

URI:

https://boris.unibe.ch/id/eprint/131225

Actions (login required)

Edit item Edit item
Provide Feedback