Title of Oral/Poster Presentation

Detecting and Revealing Malicious Retweeter Groups

Class

Article

Graduation Year

2020

College

College of Engineering

Department

Computer Science Department

Faculty Mentor

Dr. Kyumin Lee

Presentation Type

Oral Presentation

Abstract

Retweeting/sharing action has enabled information to be cascaded to distant nodes on social network. Unfortunately, malicious users as a group have taken advantage of the retweeting function with coordinated behavior to falsely distort the volume of specific keywords, topics or URLs for promotional purposes (e.g., increasing public visibility of products or services). Unfortunately, little is known about their retweeting behavior as a group and how to detect them based on group-based signals. To fill the gap, in this paper, we (i) propose Attractor+ algorithm to extract retweeter groups based on the similarity of their retweeting behavior; (ii) analyze underlying characteristics of malicious retweeter groups; (iii) propose group-based features to catch synchronized and coordinated behavior; and (iv) build a predictor to classify if a group is malicious. Experimental results show that our proposed method achieved 0.954 AUC, and improved the accuracy by 19.6~42% against existing malicious retweeter detection approaches, indicating the effectiveness of our approach.

Location

Room 154

Start Date

4-13-2017 3:00 PM

End Date

4-13-2017 4:15 PM

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Apr 13th, 3:00 PM Apr 13th, 4:15 PM

Detecting and Revealing Malicious Retweeter Groups

Room 154

Retweeting/sharing action has enabled information to be cascaded to distant nodes on social network. Unfortunately, malicious users as a group have taken advantage of the retweeting function with coordinated behavior to falsely distort the volume of specific keywords, topics or URLs for promotional purposes (e.g., increasing public visibility of products or services). Unfortunately, little is known about their retweeting behavior as a group and how to detect them based on group-based signals. To fill the gap, in this paper, we (i) propose Attractor+ algorithm to extract retweeter groups based on the similarity of their retweeting behavior; (ii) analyze underlying characteristics of malicious retweeter groups; (iii) propose group-based features to catch synchronized and coordinated behavior; and (iv) build a predictor to classify if a group is malicious. Experimental results show that our proposed method achieved 0.954 AUC, and improved the accuracy by 19.6~42% against existing malicious retweeter detection approaches, indicating the effectiveness of our approach.