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Cited by 159Misinformation and Its ImpactsSpam and Phishing DetectionComputational and Text Analysis Methods

Botometer 101: social bot practicum for computational social scientists

Open access
Yang, Kai-Cheng, Ferrara, Emilio, Menczer, Filippo·2022·Journal of Computational Social Science
DOI 10.1007/s42001-022-00177-5PMC PMC9391657
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Abstract

Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.

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Cited by

Sorted by citations

Authors
Yang, K.-C., Ferrara, E., & Menczer, F.
Affiliations
  • Indiana University Bloomington (US)
  • University of Southern California (US)
Corresponding author
Yang, Kai-Cheng
ORCID
Venue
Journal of Computational Social Science
Publisher
Springer Science and Business Media LLC
Published
2022-08-20
Volume
5
Issue
2
Pages
1511-1528
ISSN
  • 2432-2717 (ISSN-L)
  • 2432-2725
DOI
10.1007/s42001-022-00177-5
PMC
PMC9391657
OpenAlex
W4226381434
Cited by
159
References
12
Citations by year
2026: 26 · 2025: 44 · 2024: 37 · 2023: 34 · 2022: 18
FWCI
47.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Computational Social Science (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Defense Sciences Office, DARPA (W911NF-12-1-0037, W911NF-17-C-0094)
  • NSF (CCF-1101743)
  • ONR (N15A-020-0053)
  • AFOSR (FA9550-17-1-0327)
  • NIH (5R01DA039928-03)
  • James S. McDonnell Foundation (220020274)
  • Craig Newmark Philanthropies
  • Knight Foundation
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
Botometer, social bots, bot detection, Twitter, social media, computational social science

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