mlsanityData to 7 Oct 2026

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

arXiv 2501.00855 ↗First posted 1 Jan 2025cs.CY cs.AI cs.SIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 108 references that are in the map. First posted on arXiv 1 Jan 2025.

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32 of 108 references.

References · 32

  1. 2014The Rise of Social Botscited by 96
  2. 2017The spread of low-credibility content by social botscited by 73
  3. 2023Anatomy of an AI-powered malicious social botnetcited by 35
  4. 2020Detection of Novel Social Bots by Ensembles of Specialized Classifierscited by 32
  5. 2018Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaigncited by 30
  6. 2019RTbust: Exploiting Temporal Patterns for Botnet Detection on Twittercited by 27
  7. 2017Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Electioncited by 26
  8. 2018Bots increase exposure to negative and inflammatory content in online social systemscited by 26
  9. 2022Botometer 101: Social bot practicum for computational social scientistscited by 25
  10. 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 24
  11. 2021Social Bots and Social Media Manipulation in 2020: The Year in Reviewcited by 11
  12. 2022BotBuster: Multi-platform Bot Detection Using A Mixture of Expertscited by 11
20 more
  1. 2024LLMs Among Us: Generative AI Participating in Digital Discoursecited by 9
  2. 2018Unpacking the Social Media Bot: A Typology to Guide Research and Policycited by 7
  3. 2021Pro or Anti? A Social Influence Model of Online Stance Flippingcited by 6
  4. 2024An Exploratory Analysis of COVID Bot vs Human Disinformation Dissemination stemming from the Disinformation Dozen on Telegramcited by 6
  5. 2017Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accountscited by 5
  6. 2021A Method to Analyze Multiple Social Identities in Twitter Bioscited by 5
  7. 2023BotPercent: Estimating Bot Populations in Twitter Communitiescited by 5
  8. 2016Even Good Bots Fight: The Case of Wikipediacited by 4
  9. 2020Polarizing Tweets on Climate Changecited by 3
  10. 2021A Synchronized Action Framework for Responsible Detection of Coordination on Social Mediacited by 3
  11. 2022From Curious Hashtags to Polarized Effect: Profiling Coordinated Actions in Indonesian Twitter Discoursecited by 3
  12. 2024Automated versus Human Engagement: Mapping Cognitive Bias Triggers in Online Discoursecited by 3
  13. 2017Social Bots: Human-Like by Means of Human Control?cited by 2
  14. 2021How does fake news spread? Understanding pathways of disinformation spread through APIscited by 2
  15. 2023Tracking China's cross-strait bot networks against Taiwancited by 2
  16. 2023Unsupervised detection of coordinated fake-follower campaigns on social mediacited by 2
  17. 2024Cyborgs for strategic communication on social mediacited by 2
  18. 2024Assembling a Multi-Platform Ensemble Social Bot Detector with Applications to US 2020 Electionscited by 2
  19. 2018Threat or Opportunity? - Examining Social Bots in Social Media Crisis Communicationcited by 1
  20. 2024Deflating the Chinese Balloon: Types of Twitter Bots in US-China balloon incidentcited by 1

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