The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.
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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In the citing paper
“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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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
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