Wikipedia articles, tweets, and other forms of user-generated content (UGC) play an essential role in the experience of the average Web user. Outside the public eye, UGC has become equally indispensable as a source of world knowledge for systems and algorithms that help us make sense of big data. In this thesis, we demonstrate that UGC reflects the cultural diversity of its contributors to a previously unidentified extent, and that this diversity has important implications for Web users and existing UGC-based technologies. Focusing on Wikipedia, Flickr, and Twitter, we show how UGC diversity can be extracted and measured using techniques from artificial intelligence and geographic information science. Finally, through two novel applications—Omnipedia and Atlasify—we highlight the exciting potential for a new class of technologies enabled by the ability to harvest diverse perspectives from UGC.
|Commitee:||Adar, Eytan, Downey, Doug, Horn, Mike|
|School Location:||United States -- Illinois|
|Source:||DAI-B 74/07(E), Dissertation Abstracts International|
|Keywords:||Geography, Multilingual, Semantic relatedness, User-generated content, Wikipedia|
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