Abstract
Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users’ party prediction task.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 49–62 (2009)
Yan, X., Yan, L.: Gender classification of weblog authors. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 228–230 (2006)
Peersman, C., Daelemans, W., Vaerenbergh, L.V.: Predicting age and gender in online social networks. In: SMUC, pp. 37–44 (2011)
Conover, M., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of twitter users. In: Proceedings of 3rd IEEE Conference on Social Computing, SocialCom (2011)
Speel, R.W.: The evolution of republican and democratic ideologies. Journal of Policy History 12(7), 413–416 (2000)
Saunders, K., Abramowitz, A.: Ideological realignment and active partisans in the american electorate. American Politics Research 32(3), 285–309 (2004)
Fiorina, M.P., Abrams, S.J.: Political polarization in the american public. Annual Review of Political Science 11(1), 563–588 (2008)
Killian, M., Wilcox, C.: Do abortion attitudes lead to party switching? Political Research Quarterly 61(4), 561–573 (2008)
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124 (2010)
Walker, M.A., Anand, P., Abbott, R., Tree, J.E.F., Martell, C., King, J.: That is your evidence?: Classifying stance in online political debate. Decis. Support Syst. 53(4), 719–729 (2012)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: Social recommendation using probabilistic matrix factorization. In: Proc. of CIKM (2008)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM 2008 (2008)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th KDD, pp. 650–658 (2008)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents, pp. 37–44 (2010)
Mukherjee, A.: 0001, B.L.: Improving gender classification of blog authors. In: EMNLP, pp. 207–217 (2010)
Zhou, D.X., Resnick, P., Mei, Q.: Classifying the political leaning of news articles and users from user votes. In: ICWSM (2011)
Dahllöf, M.: Automatic prediction of gender, political affiliation, and age in swedish politicians from the wording of their speeches - a comparative study of classifiability. LLC 27(2), 139–153 (2012)
Durant, K.T., Smith, M.D.: Predicting the political sentiment of web log posts using supervised machine learning techniques coupled with feature selection. In: Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., Masand, B. (eds.) WebKDD 2006. LNCS (LNAI), vol. 4811, pp. 187–206. Springer, Heidelberg (2007)
Durant, K.T., Smith, M.D.: Mining sentiment classification from political web logs. In: Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th SIGKDD, WebKDD 2006 (2006)
Efron, M.: Using cocitation information to estimate political orientation in web documents. Knowl. Inf. Syst. 9(4) (2006)
Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: Proceedings of ICWSM (2010)
Abu-Jbara, A., Radev, D.: Subgroup detector: a system for detecting subgroups in online discussions. In: Proc. of the ACL 2012 Demo, pp. 133–138 (2012)
Hassan, A., Abu-Jbara, A., Radev, D.: Detecting subgroups in online discussions by modeling positive and negative relations among participants. In: Proceedings of the 2012 Joint Conference on EMNLP and CoNLL (2012)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10) (2008)
Traag, V., Bruggeman, J.: Community detection in networks with positive and negative links. Physical Review E 80(3), 036115 (2009)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW, pp. 175–186 (1994)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems (NIPS), vol. 20 (2008)
Qiu, M., Yang, L., Jiang, J.: Mining user relations from online discussions using sentiment analysis and probabilistic matrix factorization. In: NAACL, pp. 401–410. Association for Computational Linguistics (2013)
Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z., Zha, H.: Like like alike: joint friendship and interest propagation in social networks. In: WWW (2011)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. In: Artif. Intell. 2009, 4:2–4:2 (January 2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Itembased collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining, SDM 2005 (2005)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science, pp. 27–28 (2002)
Srebro, N., Jaakkola, T.: Weighted low rank approximation. In: ICML (2003)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: ICML (2008)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Abu-Jbara, A., Diab, M., Dasigi, P., Radev, D.: Subgroup detection in ideological discussions. In: Proceedings of the 50th ACL, pp. 399–409 (2012)
Glaeser, E.L., Ponzetto, G.A.M., Shapiro, J.M.: Strategic extremism: Why republicans and democrats divide on religious values. The Quarterly Journal of Economics 120(4), 1283–1330 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Gottipati, S., Qiu, M., Yang, L., Zhu, F., Jiang, J. (2013). Predicting User’s Political Party Using Ideological Stances. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-03260-3_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03259-7
Online ISBN: 978-3-319-03260-3
eBook Packages: Computer ScienceComputer Science (R0)