Mining Social Media: Tracking Content and Predicting Behavior

This explanatory thesis is concerned with user generated text, audio and video media. According to Tsagkias two aspects, the social and user generated content, led to the development of two main research branches in social media analysis. The former focuses on characterizing and modeling the network and content of each platform, while the latter aims at understanding the content dynamics for a range of applications. The chapter frames research on the second branch, providing a comprehensive portfolio of predictive analytical research, particularly the mining of social media. It begins with a review of research in social media analysis, and then discusses three tasks as part of social media mining: ranking, linking and prediction, and how they have been addressed thus far.

This piece is useful for researchers attempting to understand the complexity of social media mining, its history and how the method can be applied to the study of extremism. It showcases how data mining can be a useful strategy for predictive analytics. Researchers interested in tracking social media may very well wish to begin with Tsagkias’ article.

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