Cited by 133Spam and Phishing DetectionHate Speech and Cyberbullying DetectionMisinformation and Its Impacts

Online Social Spammer Detection

Open access
Hu, Xia, Tang, Jiliang, Liu, Huan·2014·Proceedings of the AAAI Conference on Artificial Intelligence

Abstract

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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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
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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