TY - GEN
T1 - Detecting camouflaged content polluters
AU - Wu, Liang
AU - Hu, Xia
AU - Morstatter, Fred
AU - Liu, Huan
N1 - Funding Information:
The work is funded, in part, by ONR N00014-16-1-2257 and the Department of Defense under the MINERVA initiative through the ONR N000141310835.
Publisher Copyright:
© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2017
Y1 - 2017
N2 - The connectivity and openness of the Internet have cultivated a blistering expansion of online media websites. However, the culture of openness also makes the emerging platforms an effective channel for content pollution, such as fraud, phishing, and other online abuses. To complicate the problem, content polluters actively manipulate the characteristics of the Internet through establishing links with normal users and blending the malicious information with legitimate content. The manipulated links and content, being used as camouflage, make it very intricate to detect content polluters. Recent work has investigated camouflaged fraud in networks. However, due to the lack of availability of label information for camouflaged content, it is challenging to detect content polluters with traditional approaches. In this paper, we make the first attempt on detecting camouflaged content polluters. In order to evaluate the proposed approach, we conduct experiments on real-world data. The results show that our method achieves better results than existing approaches.
AB - The connectivity and openness of the Internet have cultivated a blistering expansion of online media websites. However, the culture of openness also makes the emerging platforms an effective channel for content pollution, such as fraud, phishing, and other online abuses. To complicate the problem, content polluters actively manipulate the characteristics of the Internet through establishing links with normal users and blending the malicious information with legitimate content. The manipulated links and content, being used as camouflage, make it very intricate to detect content polluters. Recent work has investigated camouflaged fraud in networks. However, due to the lack of availability of label information for camouflaged content, it is challenging to detect content polluters with traditional approaches. In this paper, we make the first attempt on detecting camouflaged content polluters. In order to evaluate the proposed approach, we conduct experiments on real-world data. The results show that our method achieves better results than existing approaches.
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M3 - Conference contribution
AN - SCOPUS:85029459448
T3 - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
SP - 696
EP - 699
BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PB - AAAI press
T2 - 11th International Conference on Web and Social Media, ICWSM 2017
Y2 - 15 May 2017 through 18 May 2017
ER -