Media Watch

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Twittering Public Sentiments: A Predictive Analysis of Pre-Poll Twitter Popularity of Prime Ministerial Candidates for the Indian Elections 2014


Affiliations
1 Amrita Vishwa Vidyapeetham, Coimbatore, India
     

   Subscribe/Renew Journal


Twitter is a useful tool for predicting election outcomes, effectively complementing traditional opinion polling. This study undertakes a volume, sentiment and engagement analysis for predicting the popularity of Prime Ministerial candidates on Twitter as a run-up to the Indian Elections 2014. The results from a survey of 2,37,639 pre-poll tweets finds tweet volume as a significant predictor of candidate vote share, and volume and sentiments as predictors for candidate engagement levels. Higher engagement rates evolve from the horizontality of conversations about the candidate, therefore indicating a high degree of interactivity, but do not translate into a higher vote share.

Keywords

Twitter Analytics, Indian Elections 2014, Modi, Kejriwal, Rahul Gandhi, Sentiment Analysis, Twitter Engagement Rate.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Ahmed, S., & Jaidka, K. (2013). Protests against #delhigangrape on Twitter: Analyzing India's Arab Spring. eJournal of eDemocracy and Open Government, 5(1), 28-58. Retrieved from http://www.jedem.org

  • Asur, S., & Huberman, B. (2010). Predicting the future with social media. Proceedings of ACM International Conference on Web Intelligence (pp. 492-499). Toronto, Canada: IEEE. doi:10.1109/WI-IAT.2010.63

  • Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M., & Varma, V. (2012). Mining sentiments from tweets. Proceedings of 3rd Workshop on Computational Approaches to Subjetivity and Sentiment Analysis (WASSA 2012), in conjunction with Association of Computational Lingusitics (ACL 2012) (pp. 11-18). Jeju, Republic of Korea: Association for Computational Linguistics.

  • Barbera, P., & Rivero, G. (2014). Understanding the political representativeness of Twitter users. Social Science Computer Review. doi:10.1177/0894439314558836

  • Boucher, J., & Osgood, C. (1969). The Pollyanna Hypothesis. Journal of Verbal Learning and Verbal Behavior, 8(1), 1-8. doi:doi:10.1016/S0022-5371(69)80002-2

  • Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2012). Tweet your vote: How content analysis of social networks can improve our knowledge of citizen's policy preferences. An application to Italy and France. Retrieved from http://www.sisp.it/files/papers/2012/andrea-ceron-luigi-curinie-stefano-iacus-1414.pdf

  • Chung, J., & Mustafaraj, E. (2011). Can Collective Sentiment Expressed on Twitter Predict Political Elections? Proceedings of Twenty-fifth AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI Press. Retrieved from https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3549

  • CNN-IBN. (2014). IBN Live Elections Social Tracker. Retrieved from CNN-IBN Live: http://ibnlive.in.com/generalelections-2014/social-tracker/

  • Coggins, S. (2012, October 2). Twitter Metrics: Social Media Analytics (Web log Post). Retrieved from Vervely.com: http://vervely.com/twitter-metrics-social-media-analytics/2012/

  • Coviello, L., Sohn, Y., Kramer, A., Marlow, C., Franceschetti, M., & al., e. (2014). Detecting emotional contagion in massive social networks. PLoS ONE, 9(3). doi:10.1371/journal.pone.0090315

  • Dearing, J., & Rogers, E. (1988). Agenda-setting research: Where has it been, where is it going? Communication Yearbook, pp. 555-594.

  • ECI. (2014). ECI Main Page. Retrieved from ECI Web Site: eci.nic.in/eci_main1/index.aspx

  • Enjolras, B., Steen-Johnsen, K., & Wollebaek, D. (n.d.). How do social media change the conditions for civic and political mobilization. Retrieved from http://www.uio.no/english/research/interfaculty-research-areas/democracy/news-and-events/events/conferences/2012/papers-2012/steen-johnsen-elrojas-woolebaekwshop% 5D.pdf

  • Firstpost. (2014, May). Why FB, Twitter and Google are betting big on India's Elections. Retrieved from Firstpost.com: http://www.firstpost.com/politics/why-fb-twitter-and-google-are-betting-big-on-indiaselections-1510215.html

  • Garcia, D., Garas, A., & Schweitzer, F. (2012, May 10). Emotional persistence in online chatting communities. Science Reports. doi:10.1038/srep00402

  • Gayo-Avello, D., Schoen, H., Metaxas, P., Mustafaraj, E., Strohmaier, M., & Gloor, P. (2013). The power of prediction with social media. Internet Research, 23(5), 528-543. doi:10.1108/IntR-06-2013-0115

  • Gnanasambandam, M; Madgavkar, A; Kaka, N; Manyika, J; Chui, M; Bughin, J; Gomes, M. (2013). Online and Upcoming: The Internet's Impact on India. McKinsey & Company. Retrieved from http://www.mckinsey.com/search.aspx?q=Internet%27s+impact+on+India

  • Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481-510. Retrieved from http://www.jstor.org/stable/2780199

  • Habermas, J. (1989). The Structural Transformation of the Public Sphere: An Inquiry into a category of Bourgeois Society. (T. Burger, Trans.) CC BY-SA 3.0. Retrieved from en.wikipedia.org

  • IAMAI. (2014). Internet in India 2014.. Retrieved from IAMAI: http://www.iamai.in/ rsh_pay.aspx?rid=4hjkHu7GsUU=

  • India, E. (2014). Election India Opinion Polls. Retrieved from electionindia2014: http://electionindia2014.co.in

  • IRIS, & IAMAI. (2013). Social Media and Lok Sabha Elections. Retrieved from esocialsciences.org: http://www.esocialsciences.org/General/A2013412184534_19.pdf

  • Kelly, J., Barash, V., Alexanyan, K., Etling, B., Robert, G., & Palfrey, J. (2012). Mapping Russian Twitter. Berkman Center Research Publication(3). Retrieved from http://ssrn.com/abstract=2028158

  • Leavitt, A., Burchard, E., Fisher, D., & Gilbert, S. (2009). The influentials: New approaches for analyzing influence on twitter. Web Ecology Project, 4(2), 1-18. Retrieved from http://www.webecologyproject.org/ 2009/09/analyzing-influence-on-twitter/

  • Lilleker, D. (2006). Key Concepts in Political Communication. SAGE Publications.

  • McAdam, D., & Rucht, D. (1993). The Cross-national diffusion of movement ideas. The Annals of the American Academy of Political and Social Science, 528(1), 56-74. doi:10.1177/0002716293528001005

  • McCombs, M., & Shaw, D. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187. doi:10.1086/267990

  • Mehl, M. (2006). The lay assessment of subclinical depression in daily life. Psychological Assessment, 18, 340-345.

  • Metaxes, P., Mustafaraj, E., & Gayo-Avello, D. (2011). How (not) to predict elections. Proceedings of Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom) (pp. 165-171). Boston: IEEE Press. doi:10.1109/PASSAT/SocialCom.2011.98

  • Mitchell, A., & Hitlin, P. (2013). Twitter Reaction to Events often at Odds with Overall Public Opinion. Pew Research Centre. Retrieved from http://www.pewresearch.org/2013/03/04/twitter-reaction-to-eventsoften-at-odds-with-overall-public-opinion/

  • O'Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. (2010). From tweets to polls: Linking text sentiment to public opinion time series. Proceedings of the International AAAI Conference on Weblogs and Social Media (ICWSM 2010) (pp. 122-129). Washington, D.C: AAAI Press. Retrieved from http:// www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1536

  • Rogers, E. (1995). Diffusion of Innovations (Fourth Paperback ed.). New York: The Free Press Simon & Schuster Inc.

  • Sahin, I. (2006, Apr 3). Detailed review of Rogers' diffusion of innovations theory and educational technology - related studies based on Roger's theory. Turkish Online Journal of Educational Technology, 5(2), p. 1. Retrieved from http://eric.ed.gov/?q=technology+and+education&ff1=souOnline+Submission &id=ED501453

  • Sen, A. (2012). The social media as a public sphere: The rise of social opposition. Proceedings of the International Conference on Communication, Media, Technology and Design (pp. 490-494). Istanbul: ICCMTD.

  • Steffens, N., & Haslam, S. (2013). Power through 'Us': Leaders' use of we-referencing language predicts election victory. PLoS ONE, 8(10). doi:10.1371/journal.pone.0077952

  • Stieglitz, S., & Dang-Xuan, L. (2012). Social media and political communication: A social media analytics framework.

  • Social Network Analysis and Mining, 3(4), 1277-1291. Retrieved from http://dx.doi.org/10.1007/ s13278-012-0079-3

  • Tham, J., & Zanuddin, H. (2013). Malaysia's 13th general election: Political communication battle and public agenda in social media. Proceedings from the Conference Organized by Asian Network for Public Opinion Research. Seoul: ANPOR, p. 7. Retrieved from academia.edu: http://www.academia.edu/5433568/Malaysias_13th_General_Election_political_communication_battle_and_public_agenda_in_social_media

  • Toms, M. (2014, April 25). 20 million election tweets: India votes for Twitter. Hindustan Times. Retrieved from http://www.hindustantimes.com/business-news/20-million-election-tweets-india-votes-for-twitter/article1-1208135.aspx

  • Top Ten Indian Journalists to Follow on Twitter. (2013, July 30). Retrieved from Social Samosa: http:// www.socialsamosa.com/2013/07/top-ten-indian-journalists-to-follow-on-twitter/

  • Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010). Predicting elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of International AAAI Conference on Weblogs and Social Media; Fourth International AAAI Conference on Weblogs and Social Media. Washington: AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1441

  • Wu, M. (2012, March 19). Science of Social Blog.( p. 1). Retrieved from Lithium.com: http:// community.lithium.com/t5/Science-of-Social-blog/Big-Data-Big-Prediction-Looking-through-the-Predictive- Window/ba-p/41068

  • Wu, S., Hofman, J., Mason, W., & Watts, D. (2011). Who says what to whom on Twitter. Proceedings of the 20th international conference on World wide web (pp. 705-714). Hyderabad: ACM.

  • Yoon, H., & Park, H. (2011). Social media information flow and public representation: A case of South Korean politicians on Twitter. Proceedings of the 9th International Triple Helix Conference. California: Triple Helix Association.


Abstract Views: 1425

PDF Views: 2




  • Twittering Public Sentiments: A Predictive Analysis of Pre-Poll Twitter Popularity of Prime Ministerial Candidates for the Indian Elections 2014

Abstract Views: 1425  |  PDF Views: 2

Authors

Kalyani Suresh
Amrita Vishwa Vidyapeetham, Coimbatore, India
Chitra Ramakrishnan
Amrita Vishwa Vidyapeetham, Coimbatore, India

Abstract


Twitter is a useful tool for predicting election outcomes, effectively complementing traditional opinion polling. This study undertakes a volume, sentiment and engagement analysis for predicting the popularity of Prime Ministerial candidates on Twitter as a run-up to the Indian Elections 2014. The results from a survey of 2,37,639 pre-poll tweets finds tweet volume as a significant predictor of candidate vote share, and volume and sentiments as predictors for candidate engagement levels. Higher engagement rates evolve from the horizontality of conversations about the candidate, therefore indicating a high degree of interactivity, but do not translate into a higher vote share.

Keywords


Twitter Analytics, Indian Elections 2014, Modi, Kejriwal, Rahul Gandhi, Sentiment Analysis, Twitter Engagement Rate.

References





DOI: https://doi.org/10.15655/mw%2F2015%2Fv6i2%2F65670