abstract: With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, groups and organization leaders. Finally, the experimental results are presented and a subset of relevant features is identified that lead to a generalizable model. Detection of tiny number of groups from large network is achieved with 0.8 precision, 0.75 recall and 0.77 F1 score. The domain independent network and behavioral features and models developed here are suitable for solving twitter account classification problem in any context. / Dissertation/Thesis / Masters Thesis Computer Science 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:45480 |
| Date | January 2017 |
| Contributors | Gore, Chinmay Chandrashekhar (Author), Davulcu, Hasan (Advisor), Hsiao, Ihan (Committee member), Li, Baoxin (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 45 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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