# What is a Social Media Bot? A Global Comparison of Bot and Human Characteristics

arXiv: https://arxiv.org/abs/2501.00855 · HTML: https://mlsanity.com/paper/2501.00855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 108 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 30 of 32)

- [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)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [Detection of Novel Social Bots by Ensembles of Specialized Classifiers](https://mlsanity.com/paper/2006.06867/) (2020)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019)
- [Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election](https://mlsanity.com/paper/1707.00086/) (2017)
- [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)
- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Social Bots and Social Media Manipulation in 2020: The Year in Review](https://mlsanity.com/paper/2102.08436/) (2021)
- [BotBuster: Multi-platform Bot Detection Using A Mixture of Experts](https://mlsanity.com/paper/2207.13658/) (2022)
- [LLMs Among Us: Generative AI Participating in Digital Discourse](https://mlsanity.com/paper/2402.07940/) (2024)
- [Unpacking the Social Media Bot: A Typology to Guide Research and Policy](https://mlsanity.com/paper/1801.06863/) (2018)
- [Pro or Anti? A Social Influence Model of Online Stance Flipping](https://mlsanity.com/paper/2106.11076/) (2021)
- [An Exploratory Analysis of COVID Bot vs Human Disinformation Dissemination stemming from the Disinformation Dozen on Telegram](https://mlsanity.com/paper/2402.14203/) (2024)
- [Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts](https://mlsanity.com/paper/1710.04044/) (2017)
- [A Method to Analyze Multiple Social Identities in Twitter Bios](https://mlsanity.com/paper/2107.14120/) (2021)
- [BotPercent: Estimating Bot Populations in Twitter Communities](https://mlsanity.com/paper/2302.00381/) (2023)
- [Even Good Bots Fight: The Case of Wikipedia](https://mlsanity.com/paper/1609.04285/) (2016)
- [Polarizing Tweets on Climate Change](https://mlsanity.com/paper/2008.13054/) (2020)
- [A Synchronized Action Framework for Responsible Detection of Coordination on Social Media](https://mlsanity.com/paper/2105.07454/) (2021)
- [From Curious Hashtags to Polarized Effect: Profiling Coordinated Actions in Indonesian Twitter Discourse](https://mlsanity.com/paper/2207.07937/) (2022)
- [Automated versus Human Engagement: Mapping Cognitive Bias Triggers in Online Discourse](https://mlsanity.com/paper/2406.07293/) (2024)
- [Social Bots: Human-Like by Means of Human Control?](https://mlsanity.com/paper/1706.07624/) (2017)
- [How does fake news spread? Understanding pathways of disinformation spread through APIs](https://mlsanity.com/paper/2109.12865/) (2021)
- [Tracking China's cross-strait bot networks against Taiwan](https://mlsanity.com/paper/2310.10851/) (2023)
- [Unsupervised detection of coordinated fake-follower campaigns on social media](https://mlsanity.com/paper/2310.20407/) (2023)
- [Cyborgs for strategic communication on social media](https://mlsanity.com/paper/2401.06582/) (2024)
- [Assembling a Multi-Platform Ensemble Social Bot Detector with Applications to US 2020 Elections](https://mlsanity.com/paper/2401.14607/) (2024)

## Abstract (arXiv)

Chatter on social media is 20% bots and 80% humans. Chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated while humans use cues that require dialogue understanding. Bots use words that match the identities they choose to present, while humans may send messages that are not related to the identities they present. Bots and humans differ in their communication structure: sampled bots have a star interaction structure, while sampled humans have a hierarchical structure. These conclusions are based on a large-scale analysis of social media tweets across ~200mil users across 7 events. Social media bots took the world by storm when social-cybersecurity researchers realized that social media users not only consisted of humans but also of artificial agents called bots. These bots wreck havoc online by spreading disinformation and manipulating narratives. Most research on bots are based on special-purposed definitions, mostly predicated on the event studied. This article first begins by asking, "What is a bot?", and we study the underlying principles of how bots are different from humans. We develop a first-principle definition of a social media bot. With this definition as a premise, we systematically compare characteristics between bots and humans across global events, and reflect on how the software-programmed bot is an Artificial Intelligent algorithm, and its potential for evolution as technology advances. Based on our results, we provide recommendations for the use and regulation of bots. Finally, we discuss open challenges and future directions: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots.