Demystifying Misconceptions in Social Bots Research
Not on the map. Why?
Abstract (arXiv)
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. Here, we discuss a broad set of consequential methodological and conceptual issues that affect current social bots research, illustrating each with examples drawn from recent studies. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 38 of its 103 references that are in the map. First posted on arXiv 30 Mar 2023.
Builds on
38 of 103 references.
References · 38
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26 more
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Built on by
Citing papers per year: 2023: 2; 2024: 2; 2025: 3.
7 papers.
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulationscited by 3
- 2024Unmasking Social Bots: How Confident Are We?cited by 2
- 2025Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processescited by 1
- 2025Graph-based Fake Account Detection: A Surveycited by 1
- 2023Social Media Bot Detection using Dropout-GANcited by 0
- 2024BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learningcited by 0
- 2025Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?cited by 0
- 2025Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?11 Nov 2025
- 2025Graph-based Fake Account Detection: A Survey9 Jul 2025
- 2025Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes11 Mar 2025
- 2024Unmasking Social Bots: How Confident Are We?18 Jul 2024
- 2024BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning6 Feb 2024
- 2023Social Media Bot Detection using Dropout-GAN9 Nov 2023
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations24 Aug 2023