Published:
2020-06-02
Proceedings:
Proceedings of the AAAI Conference on Artificial Intelligence, 34
Volume
Issue:
Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks
Track:
Student Abstract Track
Downloads:
Abstract:
Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.
DOI:
10.1609/aaai.v34i10.7199
AAAI
Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks
ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved