# Bots increase exposure to negative and inflammatory content in online social systems

arXiv: https://arxiv.org/abs/1802.07292 · HTML: https://mlsanity.com/paper/1802.07292/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 26 of 26)

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Machine learning dismantling and early-warning signals of disintegration in complex systems](https://mlsanity.com/paper/2101.02453/) (2021)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies](https://mlsanity.com/paper/2602.07432/) (2026)
- [Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls](https://mlsanity.com/paper/2210.08786/) (2022)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [Cognitive network science for understanding online social cognitions: A brief review](https://mlsanity.com/paper/2102.12799/) (2021)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study](https://mlsanity.com/paper/2112.04913/) (2021)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification](https://mlsanity.com/paper/2208.14001/) (2022)
- [MulBot: Unsupervised Bot Detection Based on Multivariate Time Series](https://mlsanity.com/paper/2209.10361/) (2022)
- [LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies](https://mlsanity.com/paper/2604.16935/) (2026)
- [Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust](https://mlsanity.com/paper/2103.15909/) (2021)
- [For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles](https://mlsanity.com/paper/2307.03764/) (2023)
- [Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research](https://mlsanity.com/paper/2410.03545/) (2024)
- [Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News](https://mlsanity.com/paper/2605.28598/) (2026)
- [Discriminatory or Samaritan -- which AI is needed for humanity? An Evolutionary Game Theory Analysis of Hybrid Human-AI populations](https://mlsanity.com/paper/2306.17747/) (2023)
- [Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames](https://mlsanity.com/paper/2405.03688/) (2024)
- [Evaluating the Propensity of Generative AI for Producing Harmful Disinformation During the 2024 US Election Cycle](https://mlsanity.com/paper/2411.06120/) (2024)
- [What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics](https://mlsanity.com/paper/2501.00855/) (2025)
- [Public Discourse Sandbox: Facilitating Human and AI Digital Communication Research](https://mlsanity.com/paper/2505.21604/) (2025)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)

## Abstract (arXiv)

Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are no more the only players, and co-exist alongside with social bots, i.e., software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.