DocumentCode
1822117
Title
Battling the Internet water army: Detection of hidden paid posters
Author
Cheng Chen ; Kui Wu ; Srinivasan, V. ; Xudong Zhang
Author_Institution
Comput. Sci. Dept., Univ. of Victoria, Victoria, BC, Canada
fYear
2013
fDate
25-28 Aug. 2013
Firstpage
116
Lastpage
120
Abstract
We initiate a systematic study to help distinguish a special group of online users, called hidden paid posters, or termed “Internet water army” in China, from the legitimate ones. On the Internet, the paid posters represent a new type of online job opportunities. They get paid for posting comments or articles on different online communities and Websites for hidden purposes, e.g., to influence the opinion of other people towards certain social events or business markets. While being an interesting strategy in business marketing, paid posters may create a significant negative effect on the online communities, since the information from paid posters is usually not trustworthy. When two competitive companies hire paid posters to post fake news or negative comments about each other, normal netizens may feel overwhelmed and find it difficult to put any trust in the information they acquire from the Internet. In this paper, we thoroughly investigate the behavioral pattern of online paid posters based on real-world trace data. We design and validate a new detection mechanism, using both non-semantic analysis and semantic analysis, to identify potential online paid posters. Our test results with real-world datasets show a very promising performance.
Keywords
Internet; advertising data processing; social networking (online); China; Internet water army; Websites; business marketing; business markets; detection mechanism; hidden paid poster detection; netizens; nonsemantic analysis; online job opportunities; online paid posters; social events; Companies; Conferences; Educational institutions; Internet; Semantics; Social network services; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location
Niagara Falls, ON
Type
conf
Filename
6785696
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