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What’s in Twitter, I know what parties are popular and who you are supporting now!

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Abstract

In modern politics, parties and individual candidates must have an online presence and usually have dedicated social media coordinators. In this context, we study the usefulness of analysing Twitter messages to identify both the characteristics of political parties and the political leaning of users. As a case study, we collected the main stream of Twitter related to the 2010 UK General Election during the associated period—gathering around 1,150,000 messages from about 220,000 users. We examined the characteristics of the three main parties in the election and highlighted the main differences between parties. First, the retweet structure is highly clustered according to political parties. Second, users are more likely to refer to their preferred party and use more positive affect words for the party compared with other parties. Finally, the self-description of users and the List feature can reflect the political orientation of users. From these observations, we develop both an incremental and practical classification method which uses the number of Twitter messages referring to a particular political party or retweets, and a classifier leveraging the valuable semantic content of the List feature to estimate the overall political leaning of users. The experimental results showed that the proposed incremental method achieved an accuracy of 86 % for classifying the users’ political leanings and outperforms other classification methods that require expensive costs for tuning classifier parameters and/or knowledge about network topology. This advantage allows this approach to be a good candidate for social media analytics application in real time for political institution. The proposed method using List feature, in turn, achieved an accuracy of 92 %.

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Notes

  1. An online version of LIWC is available at http://www.liwc.net.

References

  • Adamic L, Glance N (2005) The political blogosphere and the 2004 U.S. election: divided they blog. In: International workshop on link discovery. Chicago, Illinois, pp 36–43

  • Aday S, Farrell H, Lynch M, Sides J, Kelly J, Zuckerman E (2010) Blogs and bullets: new media in contentious politics. Tech Rep. Issue no 65

  • Agarwal N, Liu H, Tang L, Yu PS (2012) Modeling blogger influence in a community. Soc Netw Anal Min 2(2):139–162

    Article  Google Scholar 

  • An J, Cha M, Gummadi K, Crowcroft J (2011) Media landscape in Twitter: a world of new conventions and political diversity. In: International conference on weblogs and social media. AAAI, Barcelona, Spain

  • Asur S, Huberman BA (2010) Predicting the future with social media. In: International conference on web intelligence and intelligent agent technology. vol 1, Washington, DC, USA, pp 492–499

  • Balasubramanyan R, Cohen WW, Pierce D, Redlawsk DP (2012) Modeling polarizing topics: when do different political communities respond differently to the same news? In: International conference on weblogs and social media. IAAA, Dublin, Ireland

  • Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: International conference on weblogs and social media. IAAA, San Jose, California, USA

  • Baumgartner JC, Morris JS (2010) MyFaceTube politics: social networking web sites and political engagement of young adults. Soc Sci Comput Rev 28(1):24–44

    Article  Google Scholar 

  • Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: Conference on internet measurement conference. Chicago, Illinois, USA

  • Boutet A, Kim H, Yoneki E (2012a) What’s in your tweets? i know who you supported in the UK 2010 general election. In: International conference on weblogs and social media. IAAA, Dublin, Irlande

  • Boutet A, Yoneki E, Hyoungshick K (2012b) What’s in twitter: i know what parties are popular and who you are supporting now! In: International conference on advances in social networks. Istanbul, Turkey

  • Branting K (2012) Context-sensitive detection of local community structure. Soc Netw Anal Min 2(3):279–289

    Google Scholar 

  • Cha M, Haddadi H, Benevenuto F, Gummadi P (2010) Measuring user influence in Twitter: the million follower fallacy. In: International conference on weblogs and social media. IAAA, Washington, DC, USA

  • Conover M, Ratkiewicz J, Francisco M, Gonçalves B, Flammini A, Menczer F (2011) Political polarization on twitter. In: International conference on weblogs and social media. IAAA, Barcelona, Spain

  • Diakopoulos NA, Shamma DA (2010) Characterizing debate performance via aggregated twitter sentiment. In: Conference on human factors in computing systems. Atlanta, Georgia, USA, pp 1195–1198

  • Downey E, Jones MA (2012) Public service, governance and web 2.0 technologies: future trends in social media. IGI Publishing

  • Druckman JN, Peterson E, Slothuus R (2013) How elite partisan polarization affects public opinion formation. Am Political Sci Rev 107:57–79

    Google Scholar 

  • Gayo-Avello D, Metaxas PT, Mustafaraj E (2011) Limits of electoral predictions using twitter. In: International conference on weblogs and social media. IAAA, Barcelona, Spain

  • Golbeck J, Hansen D (2011) Computing political preference among twitter followers. In: conference on human factors in computing systems. Vancouver, BC, Canada, pp 1105–1108

  • Hewstone M, Brown R (1986) Contact is not enough: an intergroup perspective on the “Contact Hypothesis”. In: Contact and Conflict in Intergroup Relations. Blackwell, UK

  • Kaschesky M, Sobkowicz P, Bouchard G (2011) Opinion mining in social media: modeling, simulating, and visualizing political opinion formation in the web. In: International Conference on Digital Government Research. College Park, MD, USA

  • Kashoob S, Caverlee J (2012) Temporal dynamics of communities in social bookmarking systems. Soc Netw Anal Min 2(4):387–404

    Google Scholar 

  • Kramer AD (2010) An unobtrusive behavioral model of “gross national happiness”. In: Conference on human factors in computing systems. Atlanta, Georgia, USA, pp 287–290

  • Lin F, Cohen WW (2008) The multirank bootstrap algorithm: Self-supervised political blog classification and ranking using semi-supervised link classification. In: International conference on weblogs and social media. IAAA, Seattle, WA, USA

  • Livne A, Simmons MP, Adar E, Adamic LA (2011) The party is over here: structure and content in the 2010 election. In: International conference on weblogs and social media. IAAA, Barcelona, Spain

  • Loukis E, Charalabidis Y (2012) Participative public policy making through multiple social media platforms utilization. Int J Electron Gov Res 8(3):78–97

    Google Scholar 

  • Mcclurg SD (2003) Social networks and political participation: the role of social interaction in explaining political participation. Political Res Q 56:449–464

    Google Scholar 

  • O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: International conference on weblogs and social media. IAAA, Washington, DC, USA

  • Pennacchiotti M, Popescu AM (2011a) Democrats, republicans and starbucks afficionados: User classification in twitter. In: International conference on knowledge discovery and data mining. San Diego, California, USA, pp 430–438

  • Pennacchiotti M, Popescu AM (2011b) A machine learning approach to twitter user classification. In: International conference on weblogs and social media. IAAA, Barcelona, Spain

  • Petersen MB, Slothuus R, Togeby L (2010) Political parties and value consistency in public opinion formation. Public Opin Q 74(3):530–550

    Google Scholar 

  • Quercia D, Ellis J, Capra L, Crowcroft J (2011) In the mood for being influential on Twitter. In: International conference on social computing. Boston, MA, USA, pp 307–314

  • Quercia D, Ellis J, Capra L, Crowcroft J (2012) Tracking gross community happiness from tweets. In: Conference on computer supported cooperative work. Seattle, Washington, USA, pp 965–968

  • Quercia D, Lambiottez R, Stillwell D, Kosinski M, Crowcroft J (2012) The personality of popular facebook users. In: Conference on computer supported cooperative work. Seattle, Washington, USA, pp 955–964

  • Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106

    Google Scholar 

  • Sarita Y, Danah B (2010) Dynamic debates: an analysis of group polarization over time on twitter. Bull Sci Technol Soc 30(5):316–327

    Google Scholar 

  • Sharma NK, Ghosh S, Benevenuto F, Ganguly N, Gummadi K (2012) Inferring who-is-who in the twitter social network. In: Workshop on online social networks. Helsinki, Finland, pp 55–60

  • Sobkowicz P, Kaschesky M, Bouchard G (2011) Opinion formation in the social web: agent-based simulations of opinion convergence and divergence. In: Agents and data mining interaction. vol 7103. Taipei, Taiwan, pp 288–303

  • Stieglitz S, Dang-Xuan L (2012) Political communication and influence through microblogging–an empirical analysis of sentiment in twitter messages and retweet behavior. In: Hawaii international conference on system sciences. IEEE Computer Society, Grand Wailea, Maui, Hawaii, pp 3500–3509

  • Stieglitz S, Dang-Xuan L (2012) Social media and political communication: a social media analytics framework. Social Network Analysis and Mining

  • Traunmüller R (2010) Web 2.0 creates a new government. In: Electronic Government and the Information Systems Perspective. Bilbao, Spain, pp 77–83

  • Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Election forecasts with twitter: How 140 characters reflect the political landscape. Soc Sci Comput Rev 29(4):402–418

    Google Scholar 

  • Vasudevan M, Deo N (2012) Efficient community identification in complex networks. Soc Netw Anal Min 2(4):345–359

    Google Scholar 

  • Wattal S, Schuff D, Mandviwalla M, Williams CB (2010) Web 2.0 and politics: the 2008 U.S. presidential election and an e-politics research agenda. MIS Q 34(4):669–688

  • Zhang W, Johnson TJ, Seltzer T, Bichard SL (2009) The Revolution Will be Networked: The Influence of Social Networking Sites on Political Attitudes and Behavior. Soc Sci Comput Rev 28(1):75–92

    Google Scholar 

  • Zhou DX, Resnick P, Mei Q (2011) Classifying the political leaning of news articles and users from user votes. In: International conference on weblogs and social media. July, IAAA, Barcelona, Spain

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Acknowledgments

This research is part-funded by the EU grants for the RECOGNITION project (FP7-ICT 257756), the EPSRC DDEPI Project, EP/H003959 and by the ERC Starting Grant GOSSPLE number 204742.

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Authors and Affiliations

  1. INRIA Rennes Bretagne Atlantique, Rennes, France

    Antoine Boutet

  2. Sungkyunkwan University, Suwon, Korea

    Hyoungshick Kim

  3. University of Cambridge, Cambridge, UK

    Eiko Yoneki

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  1. Antoine Boutet
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  2. Hyoungshick Kim
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  3. Eiko Yoneki
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Correspondence to Antoine Boutet.

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Boutet, A., Kim, H. & Yoneki, E. What’s in Twitter, I know what parties are popular and who you are supporting now!. Soc. Netw. Anal. Min. 3, 1379–1391 (2013). https://doi.org/10.1007/s13278-013-0120-1

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  • DOI: https://doi.org/10.1007/s13278-013-0120-1

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