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Visual text analytics for online conversations

University of British Columbia

With the proliferation of Web-based social media, asynchronous conversations have become very common for supporting online communication and collaboration. Yet the increasing volume and complexity of conversational data often make it very difficult to get insights about the discussions. This dissertation posits that by integrating natural language processing and information visualization techniques in a synergistic way, we can better support the user's task of exploring and analyzing conversations. Unlike most previous systems, which do not consider the specific characteristics of online conversations; we applied design study methodologies from the visualization literature to uncover the data and task abstractions that guided the development of a novel set of visual text analytics systems. The first of such systems is ConVis, that supports users in exploring an asynchronous conversation, such as a blog. ConVis offers a visual overview of a conversation by presenting topics, authors, and the thread structure of a conversation, as well as various interaction techniques such as brushing and linked highlighting. Broadening from a single conversation to a collection of conversations, MultiConVis combines a novel hierarchical topic modeling with multi-scale exploration techniques. A series of user studies revealed the significant improvements in user performance and subjective measures when these two systems were compared to traditional blog interfaces. Based on the lessons learned from these studies, this dissertation introduced an interactive topic modeling framework specifically for asynchronous conversations. The resulting systems empower the user in revising the underlying topic models through an intuitive set of interactive features when the current models are noisy and/or insufficient to support their information seeking tasks. Two summative studies suggested that these systems outperformed their counterparts that do not support interactive topic modeling along several subjective and objective measures. Finally, to demonstrate the generality and applicability of our approach, we tailored our previous systems to support information seeking in community question answering forums. The prototype was evaluated through a large-scale Web-based study, which suggests that our approach can be adapted to a specific conversational genre among a diverse range of users. The dissertation concludes with a critical reflection on our approach and considerations for future research.

Text

2017-05-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/61772

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/