TY - GEN
T1 - Battling the Internet water army
T2 - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
AU - Chen, Cheng
AU - Wu, Kui
AU - Srinivasan, Venkatesh
AU - Zhang, Xudong
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2013
Y1 - 2013
N2 - 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. Copyright 2013 ACM.
AB - 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. Copyright 2013 ACM.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84893309191&origin=recordpage
U2 - 10.1145/2492517.2492637
DO - 10.1145/2492517.2492637
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450322409
T3 - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
SP - 116
EP - 120
BT - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PB - Association for Computing Machinery
Y2 - 25 August 2013 through 28 August 2013
ER -