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Online Social Spammer Detection
Open accessAbstract
The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.
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“Different kinds of user behaviors can be observed on the Internet [6] and digital DNA is a flexible and compact way of modeling such behaviors.”
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“New waves of malicious accounts present different and advanced features, making their detection with existing systems extremely challenging [4], [6].”
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Rashid, S. M., & Hayat, M. T. (2025) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce64574.2025.11013790 · Cited by 4
Metadata
- Authors
- Hu, X., Tang, J., & Liu, H.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-19
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W113059239
- Cited by
- 133
- References
- 39
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 10 · 2022: 10 · 2021: 13 · 2020: 10 · 2019: 13 · 2018: 16 · 2017: 25 · 2016: 13 · 2015: 14 · 2014: 2
- FWCI
- 15.36
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- Office of Naval Research (N000141410095)
- Army Research Office (#025071)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- online learning, spam social media, Twitter
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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 ›
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