4.6 Article

Detecting Bots on Russian Political Twitter

Journal

BIG DATA
Volume 5, Issue 4, Pages 310-324

Publisher

MARY ANN LIEBERT, INC
DOI: 10.1089/big.2017.0038

Keywords

bot detection; ensemble methods; machine learning; Russia; Twitter

Funding

  1. INSPIRE program of the National Science Foundation [SES-1248077]
  2. New York University Global Institute for Advanced Study
  3. Moore-Sloan Data Science Environment
  4. Dean Thomas Carew's Research Investment Fund at New York University
  5. John S. and James L. Knight Foundation

Ask authors/readers for more resources

Automated and semiautomated Twitter accounts, bots, have recently gained significant public attention due to their potential interference in the political realm. In this study, we develop a methodology for detecting bots on Twitter using an ensemble of classifiers and apply it to study bot activity within political discussions in the Russian Twittersphere. We focus on the interval from February 2014 to December 2015, an especially consequential period in Russian politics. Among accounts actively Tweeting about Russian politics, we find that on the majority of days, the proportion of Tweets produced by bots exceeds 50%. We reveal bot characteristics that distinguish them from humans in this corpus, and find that the software platform used for Tweeting is among the best predictors of bots. Finally, we find suggestive evidence that one prominent activity that bots were involved in on Russian political Twitter is the spread of news stories and promotion of media who produce them.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available