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Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?
Abstract
Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.
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“some users mix manual postings with automated ones, resulting in accounts that blend human with bot behavior—dubbed cyborgs”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Chu et al., 2012
In the citing paper
“These results are backed by the same platforms where information operations took place - namely, Facebook 2 , Twitter 3 and Reddit 4 - that banned tens of thousands accounts involved in coordinated activities since 2016.”
From A decade of social bot detection (2020) · cites this paper as [3]
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Schuchard, R., Crooks, A. T., Stefanidis, A., & Croitoru, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-05414-4_34 · Cited by 23
Metadata
- Authors
- Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S.
- Affiliations
- Twitter (United States) (US)
- Mitre (United States) (US)
- William & Mary (US)
- George Mason University (US)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2012-11
- Volume
- 9
- Issue
- 6
- Pages
- 811-824
- ISSN
- 1545-5971 (ISSN-L)
- OpenAlex
- W2072715695
- Cited by
- 634
- References
- 46
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 31 · 2023: 46 · 2022: 52 · 2021: 70 · 2020: 88 · 2019: 85 · 2018: 73 · 2017: 56 · 2016: 39 · 2015: 32 · 2014: 14 · 2013: 5 · 2012: 1
- FWCI
- 45.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- Funders
- National Science Foundation
- University of Oregon
- Multidisciplinary University Research Initiative
- Air Force Office of Scientific Research
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- Twitter bots, bot detection, human vs bot classification, social media automation, spam detection, entropy features, microblogging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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