The Strength of Negative Opinions
We investigate signed social networks, in which users are connected via directional signed links indicating their opinions on each other. Predicting the sign of such links is a crucial task for many real world applications like recommendation systems. Based on the premise that like-minded users tend to influence each other more than others, we present a logistic regression classifier built on evidence drawn from the users’ ego-networks. The main focus of this work is to examine and compare the relative strength of positive and negative opinions investigating to what extent each type of link affects the overall prediction accuracy. We evaluate our approach through a thorough experimental study that comprises three large-scale real-world datasets.
Keywordssigned social networks edge sign prediction
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- 4.DuBois, T., Golbeck, J., Srinivasan, A.: Predicting trust and distrust in social networks. In: SocialCom/PASSAT, pp. 418–424. IEEE (2011)Google Scholar
- 6.Garcia, D., Garas, A., Schweitzer, F.: Positive words carry less information than negative words. CoRR, abs/1110.4123 (2011)Google Scholar
- 7.Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 403–412. ACM, New York (2004)Google Scholar
- 11.Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 1361–1370. ACM, New York (2010)Google Scholar
- 13.Massa, P., Avesani, P.: Controversial users demand local trust metrics: an experimental study on epinions.com community. In: Proceedings of the 20th National Conference on Artificial Intelligence, vol. 1, pp. 121–126. AAAI Press (2005)Google Scholar
- 14.Mishra, A., Bhattacharya, A.: Finding the bias and prestige of nodes in networks based on trust scores. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 567–576. ACM, New York (2011)Google Scholar
- 15.Provost, F.: Machine learning from imbalanced data sets 101 (extended abstract)Google Scholar
- 16.Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)Google Scholar
- 18.Zhang, J., Mani, I.: KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. In: Proceedings of the ICML 2003 Workshop on Learning from Imbalanced Datasets (2003)Google Scholar