Social Bots: Detection and Challenges
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Abstract (arXiv)
While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and actions of social bots in various online discussions to show their real-world implications and the need for detection methods. Then we discuss the challenges of bot detection methods and use Botometer, a publicly available bot detection tool, as a case study to describe recent developments in this area. We close with a practical guide on how to handle social bots in social media research.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Dec 2023.
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Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2024Depression detection in social media posts using transformer-based models and auxiliary featurescited by 2
- 2024Public interest in science or bots? Selective amplification of scientific articles on Twittercited by 0
- 2026Adversarial Creation and Detection of AI-Generated Social Bot Contentcited by 0
- 2026Adversarial Creation and Detection of AI-Generated Social Bot Content5 Jun 2026
- 2024Depression detection in social media posts using transformer-based models and auxiliary features30 Sep 2024
- 2024Public interest in science or bots? Selective amplification of scientific articles on Twitter28 Sep 2024