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PROBABILISTIC MODELS FOR IDENTIFYING AND EXPLAINING CONTROVERSY

Abstract
Navigating controversial topics on the Web encourages social awareness, supports civil discourse, and promotes critical literacy. While search of controversial topics particularly requires users to use their critical literacy skills on the content, educating people to be more critical readers is known to be a complex and long-term process. Therefore, we are in need of search engines that are equipped with techniques to help users to understand controversial topics by identifying them and explaining why they are controversial. A few approaches for identifying controversy have worked reasonably well in practice, but they are narrow in scope and exhibit limited performance. In this thesis, we first focus on understanding the theoretical grounding of the state-of-the-art algorithm. We derive an underlying probabilistic model that explains the state-of-the-art controversy detection algorithm. We revisit the properties and assumptions from the derived model, and propose new methods to identify controversy on Webpages. We then point out that the current approaches for controversy detection do not consider time while controversy is a dynamically changing phenomenon. This causes current methods to have delays in recognizing emerging controversial topics or exaggerated effects on outdated controversies. We address time-adaptable controversy detection by estimating the dynamically-changing controversy trend of topic by interpolating the observed level of contention and the public interest over time on the topic. Finally, we offer a method that explains controversy by generating a summary of each stance. Our method ranks social media postings using a score of how likely it is that the given post can be a representative summary of controversy.
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