Content Spread and User Relations in Social Computing
With the rapid growth of social media and the rise in popularity of social networks, content sharing and spreading have become the major activities for social media users. One of the valuable characteristics of social networks is its capability for user generated content to circulate rapidly through the whole network and spread influence on others. Another characteristic is its openness to everyone. It enables not only news organizations and government agencies to post information, but also ordinary citizens to post from their own perspectives and experiences. In this way, users have the access to more comprehensive and complicated information online. On one hand, social networks offer users many valuable experiences. We can take advantages of social networks such that, for example, the spread of innovation ideas can be maximized, or the expectation of users can be satisfied. On the other hand, we hope to take actions on the negative side that social networks bring to users. For example, to limit the spread of rumors and misinformation or to minimize the negative influence of cybervictims. In this dissertation, we study several problems regarding both positive and negative content spread on social network. First, we study the emerging problems of misinformation/rumor blocking and minimizing the cyberbullying influence on specific user based on Independent Cascade diffusion model and its variance Competitive Independent Cascade model. We formulate these two problems as optimization problems and design algorithms with performance guarantees. Second, we propose a content spread maximization problem and formulate the problem from a marginal gain perspective. As the considered problems are all NP-hard, we focus on the analysis of approximation results. Third, because the network structures are changing dynamically, we predict the missing links and emerging links based on community structure. Last, we study the correlations between user generated content and their roles in online discussion forum.