CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks
Abstract
Malicious crowdsourcing, also known as crowdturfing, has become an important security problem. However, detecting accounts performing crowdturfing tasks is challenging because human workers manage the crowdturfing accounts such that their characteristics are similar with the characteristics of normal accounts. In this paper, we propose a novel crowdturfing detection method, called CrowdTarget, that aims to detect target objects of crowdturfing tasks (e.g., post, page, and URL) not accounts performing the tasks. We identify that the manipulation patterns of target objects by crowdturfing workers are unique features to distinguish them from normal objects. We apply CrowdTarget to detect collusion-based crowdturfing services to manipulate account popularity on Twitter with artificial retweets. Evaluation results show that CrowdTarget can accurately distinguish tweets receiving crowdturfing retweets from normal tweets. When we fix the false-positive rate at 0.01, the best true-positive rate is up to 0.98.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions.
“Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions, e.g., that leverage lockstep behaviors [32, 43, 51, 72, 73, 78, 85, 86, 89, 92, 93] or review bursts [27, 28, 35, 39, 40, 42, 44, 45, 50-53, 53, 5759, 65, 66, 84, 87, 88, 92].”
From RacketStore (2021) · cites this paper as [72]
Some providers generate fake popularity signals using automated bots, while others use realistic accounts and even crowdsourced real humans.
“some providers generate fake popularity signals using automated bots (aka. sybils), but some others use realistic accounts and even crowdsourced real humans (aka. crowdturfing) [58, 83, 84, 93, 100].”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [83]
Supervised machine learning models can detect fraudulent activities with high accuracy given a ground truth dataset and reasonable feature engineering.
“With a ground truth dataset and reasonable feature engineering, it is often possible to build high accuracy supervised machine learning models to detect such activities”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [83]
The citation lists haven’t been fetched yet
References 27 · Cited by 50
- Authors
- Song, J., Lee, S., & Kim, J.
- Affiliations
- Pohang University of Science and Technology (KR)
- Pohang University of Science and Technology (POSTECH), Pohang, South Korea
- ORCID
- Kim, Jong
- Venue
- Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2015-10-12
- Pages
- 793-804
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2025440088
- Semantic Scholar
- c91b460e70a43483832731757360aecbe44a9973
- Cited by
- 50
- References
- 27
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 7 · 2022: 4 · 2021: 2 · 2020: 9 · 2019: 10 · 2018: 4 · 2017: 8 · 2016: 2
- FWCI
- 4.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- Funders
- ICT R&D program of MSIP/IITP (14-824-09-013)
- 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 › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
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
