Probing the Limits of Social Data: Biases, Methods, and Domain Knowledge

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection techniques. This has led to numerous studies that leverage online social data to model or gain insights about real world phenomena, as well as to inform system or methods design for performance gains, or for providing personalized services. Alas, regardless of how large, detailed or varied the online social data is, there are limits to what can be discerned from it about real-world, or even media- or application-specific phenomena. This thesis investigates four instances of such limits that are related to both the properties of the working data sets and of the methods used to acquire and leverage them, including: (1) online social media biases, (2) assessing and (3) reducing data collection biases, and (4) methods sensitivity to data biases and variability. For each of them, we conduct a separate case study that enables us to systematically devise and apply consistent methodologies to collect, process, compare or assess different data sets and dedicated methods. The main contributions of this thesis are: (i) To gain insights into media-specific biases, we run a comparative study juxtaposing social and mainstream media coverage of domain-specific news events for a period of 17 months. To this end, we introduce a generic methodology for comparing news agendas online based on a comparison of spikes of coverage. We expose significant differences in the type of events that are covered by the two media. (ii) To assess possible biases across data collections, we run a transversal study that systematically assembles and examines 26 distinct data sets of social media posts during a variety of crisis events spanning a 2 years period. While we find patterns and consistencies, we also uncover substantial variability across different event data sets, highlighting the pitfalls of generalizing findings from one data set to another. (iii) To improve data collections, we introduce a method that increases the recall of social media samples, while preserving the original distribution of message types and sources. To locate and monitor domain-specific events, this method constructs and applies a domain-specific, yet generic lexicon, automatically learning event-specific terms and adapting the lexicon to the targeted event. The resulted improvements also show that only a fraction of the relevant data is currently mined. (iv) To test the methods sensitivity, to data biases and variability we run an empirical evaluation on 6 real-world data sets dissecting the impact of user and item attributes on the performance of recommendation approaches that leverage distinct social cues--explicit social links vs. implicit interest affinity. We show performance variations not only across data sets, but also within each data set, across different classes of users or items, suggesting that global metrics are often unsuited for assessing recommendation systems performance. The overarching goal of this thesis is to contribute a practical perspective to the body of research that aims to quantify biases, to devise better methods to collect and model social data, and to evaluate such methods in context.

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