@inproceedings{0604371a2d4c430ea137e8d4086734b6,
title = "Community detection in political Twitter networks using Nonnegative Matrix Factorization methods",
abstract = "Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity-only methods which suffer from detection of artificially large number of clusters for Twitter networks.",
author = "Mert Ozer and Nyunsu Kim and Hasan Davulcu",
note = "Funding Information: This research was supported by ONR Grants N00014-16-1-2386 and N00014-15-1-2722 Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",
year = "2016",
month = nov,
day = "21",
doi = "10.1109/ASONAM.2016.7752217",
language = "English (US)",
series = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81--88",
editor = "Ravi Kumar and James Caverlee and Hanghang Tong",
booktitle = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",
}