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. 2022;5(2):1511-1528.
doi: 10.1007/s42001-022-00177-5. Epub 2022 Aug 20.

Botometer 101: social bot practicum for computational social scientists

Kai-Cheng Yang  1 , Emilio Ferrara  2 , Filippo Menczer  1
Affiliations

Botometer 101: social bot practicum for computational social scientists

Kai-Cheng Yang et al. J Comput Soc Sci. 2022.

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.

Keywords: Bot detection; Botometer; Social bots; Twitter.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The timeline of Botometer versions
Fig. 2
Fig. 2
Percentage of accounts using each language in the three datasets combined
Fig. 3
Fig. 3
a Bot score distributions for tweets mentioning different cashtags. b Percentage of tweets posted by likely bots using 0.5 as a threshold. c Box plots of the bot scores for tweets mentioning different cashtags. The white lines indicate the median values; the white dots indicate the mean values. d Similar to b but using a bot score threshold of 0.7. Statistical tests are performed for pairs of results in b–d. Significance level is represented by the stars: ***p≤0.001, **p≤0.01, *p≤0.05, NS=p>0.05
Fig. 4
Fig. 4
Screenshot of a bot-like account replying to a tweet containing the keyword “NFT” with a message promoting cryptocurrencies. The same message was replied by this account to a large number of tweets
Fig. 5
Fig. 5
Time series of bot scores of an account from September 2020 to November 2021. The queries were not made regularly, so the time intervals between consecutive data points vary
See this image and copyright information in PMC

References

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