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

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?

Chu, Zi, Gianvecchio, Steven, Wang, Haining, Jajodia, Sushil·2012·IEEE Transactions on Dependable and Secure Computing
DOI 10.1109/tdsc.2012.75
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/tdsc.2012.75

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 46 references

Research works printed but not linked: 7

  1. 2011, The Top 500 Sites on the Web by Alexa
  2. Sutton, Back-Channels on the Front Lines: Emerging Use of Social Media in the 2007 Southern California Wildfires
  3. 2010, Twitter api Wiki
  4. 2010, Automatic Twitter Software
  5. 10.1007/s004220050414 · DOI
  6. 2010, Google Safe Browsing API
  7. Kohavi, 1999, Decision Tree Discovery, 267

Cited by

Sorted by citations

See all 634

Related

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)
ORCID
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

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 ›

Are you an author, or do you see a problem with this page? Request a review