# Demystifying Misconceptions in Social Bots Research

arXiv: https://arxiv.org/abs/2303.17251 · HTML: https://mlsanity.com/paper/2303.17251/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 103 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 30 of 38)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [The DARPA Twitter Bot Challenge](https://mlsanity.com/paper/1601.05140/) (2016)
- [Charting the Landscape of Online Cryptocurrency Manipulation](https://mlsanity.com/paper/2001.10289/) (2020)
- [LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers](https://mlsanity.com/paper/1809.09684/) (2018)
- [Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal](https://mlsanity.com/paper/2104.10635/) (2021)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [Characterizing the 2016 Russian IRA Influence Campaign](https://mlsanity.com/paper/1812.01997/) (2018)
- [Botivist: Calling Volunteers to Action Using Online Bots](https://mlsanity.com/paper/1509.06026/) (2015)
- [Dissecting a Social Botnet: Growth, Content and Influence in Twitter](https://mlsanity.com/paper/1604.03627/) (2016)
- [Unpacking the Social Media Bot: A Typology to Guide Research and Policy](https://mlsanity.com/paper/1801.06863/) (2018)
- [Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter](https://mlsanity.com/paper/1804.04406/) (2018)
- [The role of bot squads in the political propaganda on Twitter](https://mlsanity.com/paper/1905.12687/) (2019)
- [Manipulating Twitter Through Deletions](https://mlsanity.com/paper/2203.13893/) (2022)
- [The Rise of Social Botnets: Attacks and Countermeasures](https://mlsanity.com/paper/1603.02714/) (2016)

## Built on by (most cited first, 7 of 7)

- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [Unmasking Social Bots: How Confident Are We?](https://mlsanity.com/paper/2407.13929/) (2024)
- [Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes](https://mlsanity.com/paper/2503.09626/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Social Media Bot Detection using Dropout-GAN](https://mlsanity.com/paper/2311.05079/) (2023)
- [BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2402.03740/) (2024)
- [Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?](https://mlsanity.com/paper/2511.08455/) (2025)

## 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.