Detecting spammers and content promoters in online video social networks
Abstract
A number of online video social networks, out of which YouTube is the most popular, provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users--promoters--may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem.
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During Hurricane Sandy, malicious entities widely exploited social media to spread rumors and fake pictures in real-time.
“Social media was also widely exploited by malicious entities during Sandy, to spread rumors and fake pictures in real-time. 2 3”
From Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy (2013) · cites this paper as [3]
Cites
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Measurement and analysis of online social networks
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Spotting fake reviewer groups in consumer reviews
Mukherjee, A., Liu, B., & Glance, N. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187863 · Cited by 773 · see citing works
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- Authors
- Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M.
- Affiliations
- Federal University of Minas Gerais, Belo Horizonte, Brazil
- Universidade Federal de Minas Gerais (BR)
- Venue
- Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2009-07-19
- Pages
- 620-627
- OpenAlex
- W2097436041
- Cited by
- 220
- References
- 2
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 6 · 2022: 7 · 2021: 11 · 2020: 12 · 2019: 5 · 2018: 18 · 2017: 20 · 2016: 19 · 2015: 19 · 2014: 20 · 2013: 22 · 2012: 23
- FWCI
- 13.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- 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
- spam detection, YouTube, video ranking, user-generated content
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.
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