A large part of the Web, today, consists of online platforms that allow their users to generate digital content. They include online social networks, multimedia-sharing websites, blogging platforms, and online discussion boards, to name a few examples. Users of those platforms generate content in the form of digital items (e.g. documents, images, or videos), inspect content generated by others, and, finally, interact with each other (e.g. by commenting on each other's generated items). For the social process of information exchange they enable, such platforms are customarily referred to as `social media'.
Activity on social media is largely spontaneous and uncoordinated, but it is not random; users choose the discussions they engage in and who they interact with, and their choices and actions reflect what they find important. In this thesis, we define and quantify notions of importance for items, users, and social connections between users, and, based on those definitions, propose efficient algorithms to detect important instances of social media activity. Our description of the algorithms is accompanied with experimental studies that showcase their performance on real datasets in terms of efficiency and effectiveness.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/44143 |
| Date | 02 April 2014 |
| Creators | Mathioudakis, Michail |
| Contributors | Koudas, Nick |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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