ABSTRACT
We present a novel sentiment classifier particularly designed for modeling and analyzing social movements; capturing levels of support (supportive versus non-supportive) and degrees of enthusiasm (enthusiastic versus passive). The resulting computational solution can help organizations involved with social causes to disseminate messages in a more informed and effective fashion; potentially leading to greater impact. Our findings suggest that enthusiastic and supportive tweets are more prevalent in tweets about social causes than other types of tweets on Twitter.
- Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10, 10--17. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 65--74). ACM. Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169--2188. Tinati, R., Carr, L., Hall, W., & Bentwood, J. (2012, April). Identifying communicator roles in twitter. In Proceedings of the 21st international conference companion on World Wide Web (pp. 1161--1168). ACM Sannella, M. J. 1994. Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79--86). Association for Computational Linguistics. Google ScholarDigital Library
Index Terms
- Enthusiasm and support: alternative sentiment classification for social movements on social media
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