One of the key features driving the growth and success of the Social Web is large-scale participation through user-contributed content – often through short text in social media. Unlike traditional long-form documents – e.g., Web pages, blog posts – these short text resources are typically quite brief (on the order of 100s of characters), often of a personal nature (reflecting opinions and reactions of users), and being generated at an explosive rate. Coupled with this explosion of short text in social media is the need for new methods to organize, monitor, and distill relevant information from these large-scale social systems, even in the face of the inherent “messiness” of short text, considering the wide variability in quality, style, and substance of short text generated by a legion of Social Web participants.
Hence, this dissertation seeks to develop new algorithms and methods to ensure the continued growth of the Social Web by enhancing how users engage with short text in social media. Concretely, this dissertation takes a three-fold approach:
First, this dissertation develops a learning-based algorithm to automatically rank short text comments associated with a Social Web object (e.g., Web document, image, video) based on the expressed preferences of the community itself, so that low-quality short text may be filtered and user attention may be focused on highly-ranked short text.
Second, this dissertation organizes short text through labeling, via a graph- based framework for automatically assigning relevant labels to short text. In this way meaningful semantic descriptors may be assigned to short text for improved classification, browsing, and visualization.
Third, this dissertation presents a cluster-based summarization approach for extracting high-quality viewpoints expressed in a collection of short text, while maintaining diverse viewpoints. By summarizing short text, user attention may quickly assess the aggregate viewpoints expressed in a collection of short text, without the need to scan each of possibly thousands of short text items.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/149337 |
| Date | 03 October 2013 |
| Creators | Khabiri, Elham |
| Contributors | Caverlee, James, Shipman, Frank, Gutierrez Osuna, Ricardo, Burkart, Patrick |
| Source Sets | Texas A and M University |
| Language | English |
| Detected Language | English |
| Type | Thesis, text |
| Format | application/pdf |
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