# BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots

arXiv: https://arxiv.org/abs/2601.06154 · HTML: https://mlsanity.com/paper/2601.06154/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 90 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [Social Bots and Social Media Manipulation in 2020: The Year in Review](https://mlsanity.com/paper/2102.08436/) (2021)
- [Science vs Conspiracy: collective narratives in the age of (mis)information](https://mlsanity.com/paper/1408.1667/) (2014)
- [Modeling Information Propagation with Survival Theory](https://mlsanity.com/paper/1305.3616/) (2013)
- [Unpacking the Social Media Bot: A Typology to Guide Research and Policy](https://mlsanity.com/paper/1801.06863/) (2018)
- [Simulation of Stance Perturbations](https://mlsanity.com/paper/2307.08511/) (2023)
- [Automated versus Human Engagement: Mapping Cognitive Bias Triggers in Online Discourse](https://mlsanity.com/paper/2406.07293/) (2024)
- [The Dual Personas of Social Media Bots](https://mlsanity.com/paper/2504.12498/) (2025)
- [Cyborgs for strategic communication on social media](https://mlsanity.com/paper/2401.06582/) (2024)
- [Local Interactions and the Emergence of a Twitter Small-World Network](https://mlsanity.com/paper/1508.03594/) (2015)
- [Social Bots for Online Public Health Interventions](https://mlsanity.com/paper/1804.07886/) (2018)
- [Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types](https://mlsanity.com/paper/1810.01501/) (2018)
- [Down the bot hole: actionable insights from a 1-year analysis of bots activity on Twitter](https://mlsanity.com/paper/2010.15820/) (2020)

## Built on by (most cited first, 1 of 1)

- [Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model](https://mlsanity.com/paper/2608.00929/) (2026)

## Abstract (arXiv)

Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiracies in an automated fashion. Currently, organizations combat the spread of conspiracies through manual fact-checking processes and the dissemination of counter-narratives. However, the effects of harnessing the same automation to create useful bots are not well explored. To address this, we create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network. These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging. The simulated agents interact through generating, consuming and propagating information. Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots; however, Good Bots are more efficient and sustainable than Info-Correction Bots . Proactive good messaging is more resource-effective than reactive information correction. With our observations, we expand the concept of bots as a malicious social media agent towards automated social media agent that can be used for both good and bad purposes. These results have implications for designing communication strategies to maintain a healthy social cyber ecosystem.