Battling the Internet Water Army: Detection of Hidden Paid Posters

TL;DR

This paper presents a new detection method for identifying hidden paid posters online, using behavioral analysis and semantic techniques, validated on real-world data with promising results.

Contribution

It introduces a systematic approach combining behavioral and semantic analysis to detect paid posters, addressing a significant challenge in online community trust.

Findings

High detection accuracy on real-world datasets

Effective differentiation between paid posters and legitimate users

Potential to improve online community trustworthiness

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 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.