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In online social media, users become information creators and disseminators through the active interplay between information items and other users, instead of just being information consumers of a decade ago. This kind of information production and dissemination in collaborative and active manner further aggravates the problem of information overload on the World Wide Web (WWW). The existing approaches for information retrieval (IR) and natural language processing (NLP) tasks often offer an intolerable response time for Web users. Moreover, given the numerous interactions between users and information items, new kinds of information needs are emerging, such as opinion mining, event detection and summarization, etc. However, the existing IR technologies (based on bag-of-word model), and NLP technologies (based on the linguistical features), often fail to satisfy the web users in these emerging information needs. On the other hand, people participate in online social media to share stories, photos with their friends, vote and leave opinions, or tag web pages, and so on. The digital footprints of these behaviors make online social media semantic resources which we can exploit to better understand and organize the astronomical information. In this dissertation, we first analyze online social media as multi-dimensional social network by taking Wikipedia as a case study. We find that given the multiple relations exposed from different perspectives in the network, focusing on only one specific relation could lead to biased or even wrong conclusion. Traditional information retrieval approaches are mainly bag-of-word model and keyword based, which ignore the word

Harnessing online social media to deal with information overload.