Quantifying political leaning from tweets and retweets

Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013

Volumn , Issue , 2013, Pages 640-649

  Wong, Felix Ming Fai ^a   Tan, Chee Wei ^b   Sen, Soumya ^a   Chiang, Mung ^a  

^a PRINCETON UNIVERSITY   (United States)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; ELECTRIC NETWORK TOPOLOGY; INVERSE PROBLEMS; SOCIAL NETWORKING (ONLINE);

EXPLICIT KNOWLEDGE; LINEAR INVERSE PROBLEMS; MEDIA OUTLETS; NETWORK TOPOLOGY; NEWS COVERAGE; ON-LINE SOCIAL NETWORKS; PRESIDENTIAL ELECTION; SCALABLE APPROACH;

SOCIAL SCIENCES;

EID: 84900456878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Adamic, L. A., and Glance, N. 2005. The political blogo-sphere and the 2004 U.S. election: Divided they blog. In Proc. LinkKDD.
2. Al Zamal, F.; Liu, W.; and Ruths, D. 2012. Homophily and latent attribute inference: Inferring latent attributes of Twitter users from neighbors. In Proc. ICWSM.
3. An, J.; Cha, M.; Gummadi, K. P.; Crowcroft, J.; and Quercia, D. 2012. Visualizing media bias through Twitter. In Proc. ICWSM SocMedNews Workshop.
4. Ansolabehere, S.; Lessem, R.; and Snyder, J. M. 2006. The orientation of newspaper endorsements in U.S. elections. Quarterly Journal of Political Science 1(4):393-404.
5. Barbera, P. 2012. A new measure of party identification in Twitter. evidence from Spain. In Proc. 2nd Annual General Conference of EPSA.
6. Bollen, J.; Pepe, A.; and Mao, H. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proc. ICWSM.
7. Boutet, A.; Kim, H.; and Yoneki, E. 2012. What's in your tweets? I know who you supported in the UK 2010 general election. In Proc. ICWSM.
8. Boyd, S., and Vandenberghe, L. 2004. Convex Optimization. Cambridge University Press.
9. Clinton, J.; Jackman, S.; and Rivers, D. 2004. The statistical analysis of roll call data. American Political Science Review 98(2):355-370.
10. Conover, M. D.; Gonçalves, B.; Ratkiewicz, J.; Flammini, A.; and Menczer, F. 2011a. Predicting the political alignment of Twitter users. In Proc. IEEE SocialCom.
11. Conover, M. D.; Ratkiewicz, J.; Francisco, M.; Gonçalves, B.; Flammini, A.; and Menczer, F. 2011b. Political polarization on Twitter. In Proc. ICWSM.
12. CVX Research, Inc. 2012. CVX: Matlab software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx.
13. Gayo-Avello, D.; Metaxas, P. T; and Mustafaraj, E. 2011. Limits of electoral predictions using Twitter. In Proc. ICWSM.
14. Gentzkow, M., and Shapiro, J. M. 2010. What drives media slant? Evidence from U.S. daily newspapers. Econometrica 78(1):35-71.
15. Gerrish, S., and Blei, D. 2011. Predicting legislative roll calls from text. In Proc. ICML.
16. Gerrish, S., and Blei, D. 2012. How the vote: Issue-adjusted models of legislative behavior. In Proc. NIPS.
17. Golbeck, J., and Hansen, D. L. 2012. Computing political preference among Twitter followers. In Proc. CHI.
18. Groseclose, T., and Milyo, J. 2005. A measure of media bias. The Quarterly Journal of Economics 120(4):1191-1237.
19. Ho, D. E., and Quinn, K. M. 2008. Measuring explicit political positions of media. Quarterly Journal of Political Science 3(4):353-377.
20. King, A. S.; Orlando, F. J.; and Sparks, D. B. 2011. Ideological extremity and primary success: A social network approach. In Proc. MPSA Conference.
21. Livne, A.; Simmons, M. P.; Adar, E.; and Adamic, L. A. 2011. The party is over here: Structure and content in the 2010 election. In Proc. ICWSM.
22. Lott, J. R., and Hassett, K. A. 2004. Is newspaper coverage of economic events politically biased? Online. http://dx.doi.org/10.2139/ssrn.588453.
23. Lumezanu, C.; Feamster, N.; and Klein, H. 2012. #bias: Measuring the tweeting behavior of propagandists. In Proc. ICWSM.
24. Maynard, D., and Funk, A. 2011. Automatic detection of political opinions in tweets. In Proc. The Semantic Web: ESWC 2011 Workshops.
25. Mejova, Y.; Srinivasan, P.; and Boynton, B. 2013. GOP primary season on twitter: "Popular" political sentiment in social media. In Proc. WSDM.
26. Metaxas, P. T., and Mustafaraj, E. 2012. Social media and the elections. Science 338:472-473.
27. Mustafaraj, E.; Finn, S.; Whitlock, C.; and Metaxas, P. T. 2011. Vocal minority versus silent majority: Discovering the opinions of the long tail. In Proc. SocialCom/PASSAT.
28. O'Connor, B.; Balasubramanyan, R.; Routledge, B. R.; and Smith, N. A. 2010. From tweets to polls: Linking text sentiment to public opinion time series. In Proc. ICWSM.
29. Park, S.; Ko, M.; Kim, J.; Liu, Y.; and Song, J. 2011. The politics of comments: Predicting political orientation of news stories with commenters' sentiment patterns. In Proc. CSCW.
30. Pennacchiotti, M., and Popescu, A.-M. 2011. A machine learning approach to Twitter user classification. In Proc. ICWSM.
31. Poole, K. T., and Rosenthal, H. 1985. A spatial model for legislative roll call analysis. American Journal of Political Science 29(2):357-384.
32. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; and A., K. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61(12):2544-2558.
33. Tumasjan, A.; Sprenger, T. O.; Sandner, P. G.; and Welpe, I. M. 2010. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proc. ICWSM.
34. Weber, I.; Garimella, V. R. K.; and Borra, E. 2012. Mining web query logs to analyze political issues. In Proc. WebSci.
35. Zhou, D. X.; Resnick, P.; and Mei, Q. 2011. Classifying the political leaning of news articles and users from user votes. In Proc. ICWSM