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Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
conference contribution
posted on 2015-08-25, 00:00 authored by Adham Beykikhoshk, Ognjen Arandjelovic, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshNotwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.
History
Event
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2015 : Paris, France)Pagination
1354 - 1361Publisher
Association for Computing Machinery (ACM)Location
Paris, FrancePlace of publication
New York, N.Y.Publisher DOI
Start date
2015-08-25End date
2015-08-28ISBN-13
9781450338547Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, ACMEditor/Contributor(s)
J Pei, F Silvestri, J TangTitle of proceedings
ASONAM 2015: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningUsage metrics
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