Online social media, such as news websites and community question answering (CQA) portals, have made useful information accessible to more people. However, many of online comment areas and communities are flooded with fraudulent information. These messages come from a special group of online users, called online paid posters, or termed "Internet water army" in China, represents a new type of online job opportunities. Online paid posters get paid for posting comments or articles on different online communities and websites for hidden purpose, 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. We thoroughly investigate the behavioral pattern of online paid posters based on a real-world trace data from the social comments of a business conflict. We design and validate a new detection mechanism, including both non-semantic analysis and semantic analysis, to identify potential online paid posters. Using supervised and unsupervised approaches, our test results with real-world datasets show a very promising performance. / Graduate

Recommendation systems are information filtering systems that help users effectively and efficiently explore large amount of information and identify items of interest. Accurate predictions of users' interests improve user satisfaction and are beneficial to business or service providers. Researchers have been making tremendous efforts to improve the accuracy of recommendations. Emerging trends of technologies and application scenarios, however, lead to challenges other than accuracy for recommendation systems. Three new challenges include: (1) opinion spam results in untrustworthy content and makes recommendations deceptive; (2) users prefer diversified content; (3) in some applications user behavior data may not be available to infer users' preference. This thesis tackles the above challenges. We identify features of untrustworthy commercial campaigns on a question and answer website, and adopt machine learning-based techniques to implement an adaptive detection system which automatically detects commercial campaigns. We incorporate diversity requirements into a classic theoretical model and develop efficient algorithms with performance guarantees. We propose a novel and robust approach to infer user preference profile from recommendations using copula models. The proposed approach can offer in-depth business intelligence for physical stores that depend on Wi-Fi hotspots for mobile advertisement. / Graduate / 0984 / cchenv@uvic.ca