ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Large-scale sparse logistic regression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A System for Ranking Organizations Using Social Scale Analysis
EISIC '11 Proceedings of the 2011 European Intelligence and Security Informatics Conference
LookingGlass: a visual intelligence platform for tracking online social movements
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Internet and social media devices created a new public space for online debate on political and social topics. A debate is defined as a formal discussion on a set of related topics in a public meeting, in which opposing perspectives and arguments are put forward. In this paper, we develop automated perspective discovery techniques which would contribute to the understanding of features (i.e. social, political, cultural, religious beliefs, goals, and practices) shared by each side of the debate. Secondly, we show that, compared to a semi-automated process, our perspective discovery algorithms not only identify larger number of relevant features, but they also yield a higher accuracy scaling of moderate to extreme organizations on both sides of a debate.