Cited by 634Spam and Phishing DetectionMisinformation and Its ImpactsUser Authentication and Security Systems

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Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

Chu, Zi, Gianvecchio, Steven, Wang, Haining, Jajodia, Sushil·2012·IEEE Transactions on Dependable and Secure Computing

DOI 10.1109/tdsc.2012.75

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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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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)
ORCID: Wang, Haining
Jajodia, Sushil
Venue: IEEE Transactions on Dependable and Secure Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2012-11
Volume: 9
Issue: 6
Pages: 811-824
ISSN: 1545-5971 (ISSN-L)
DOI: 10.1109/tdsc.2012.75
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

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