  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 opportunity. They get paid for posting comments and new
threads or articles on different online communities and websites for some
hidden purposes, e.g., to influence the opinion of other people towards certain
social events or business markets. Though 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 online users 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.
