UBC Theses and Dissertations

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UBC Theses and Dissertations

Detection, estimation and control in online social media Aprem, Anup


Due to large scale use of online social media there has been growing interest in modeling and analysis of data from online social media. The unifying theme of this thesis is to develop a set of mathematical tools for detection, estimation and control in online social media. The following are the main contributions of this thesis: Chapter 2 deals with nonparametric change detection for dynamic utility maximization agents. Using the revealed preference framework, necessary and sufficient conditions for detecting the change point are derived. In the presence of noisy measurements, we construct a decision test to check for dynamic utility maximization behaviour and the change point. Experiments on the Yahoo! Tech Buzz dataset show that the framework can be used to detect changes in ground truth using online search data. Chapter 3 studies engagement dynamics and sensitivity analysis of YouTube videos. Using machine learning and sensitivity analysis techniques it is shown that the video view count is sensitive to 5 meta-level features. In addition, changing the meta-level after the video has been posted increases the popularity of the video. In addition, we examine how the social dynamics of a YouTube channel affect it's popularity. The results are empirically validated on a real-world data consisting of about 6 million videos spread over 25 thousand channels. Chapter 4 considers the problem of scheduling advertisements in live personalized online social media. Broadcasters aim to opportunistically schedule advertisements (ads) so as to generate maximum revenue. The problem is formulated as a multiple stopping problem and is addressed in a partially observed Markov decision process (POMDP) framework. Structural results are provided on the optimal ad scheduling policy. By exploiting the structure of the optimal policy, optimum linear threshold policies are computed using a stochastic gradient algorithm. The proposed model and framework are validated on a Periscope dataset and it was found that the revenue can be improved by 25% in comparison to currently employed periodic scheduling.

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