Earticle

초록

영어

Spamming on microblogging platforms has been a critical issue in microblog-based applications, because spamming has a significant impact on information quality and credibility. In this paper, we characterize two types of spammers on microblogging platforms, namely advertised spammers and following spammers, and then present preliminary approaches to detect these spammers. We first use a real data set to characterize the features of AS and FS in terms of various aspects such as profile, behavior, and social relationship. Specially, we introduce a new feature named duplication for FS detection, which describes the duplicated behavior of users in sharing information on microblogging platforms. We present a content-sharing graph to model the relationship between users and microblogging contents, and propose an effective algorithm to calculate the duplication feature. We run several classification methods on the characterized features to test the effectiveness of the features in AS and FS detection. The results w.r.t. precision, recall, F-measure, and ROC suggest the effectiveness of our proposed features. In particular, the duplication feature is able to improve the effectiveness of FS detection.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Feature Analysis
  3.1. Data Set
  3.2. Profile Analysis
  3.3. Behavior Analysis
  3.4. Social Relationship
 4. Content Share Graph
  4.1. Posts with Same Content
  4.2. Build Graph
  4.3. Page Rank Based Spam Detection
 5. Experiments
  5.1. Experimental Setting
  5.2. Results
 6. Conclusion
 References

키워드

저자

• Jie Zhao [ School of Business, Anhui University ]
• Yan Liu [ School of Business, Anhui University ]
• Shuhan Liu [ School of Business, Anhui University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605