Election Prediction On Social Media
Keywords:
Social media, performance indicators, sentiment analysisAbstract
Social media today is the most popular medium of communication, due to its immediacy. According to Statista, the number of social media users in India is 226 million (2018) and this is expected to go up to 336 million by 2021. The 2014 Lok Sabha elections witnessed a significant usage of social media by political parties and leaders, especially the BJP and their then PM designate Narendra Modi to disseminate their ideology, policies and programmes and highlight the shortcomings / corruption-related scandals of the previous regime. All this helped in creating what is called the ‗Modi wave‘, and led to BJP sweeping the 2014 polls. After 2014, most political parties realised the importance of social media and registered their presence on platforms like Facebook, Twitter, Instagram. About 65 percent of India‘s population is within the age group 18-35. This group spends almost 4 hours on the internet. Political parties are therefore targeting this group of voters for mobilisation, as most of them use Twitter / Facebook to consume news. This paper represents various issues, methodologies, techniques and research work carried out for election prediction
References
[1]Kaplan, A.M and Haenlein, M. “Users of the world, unite! The challenges and opportunities of Social Media.” Business Horizons 53, 59-68, 2010.
[2]Hinton, S. and Hjorth, L. “Understanding Social Media” 1st edition, SAGE Publications, UK, London, 2013.
[3] Gil de Zúñiga, H., Molyneux, L. & Zheng, P. “Social Media, Political Expression, and Political Participation: Panel Analysis of Lagged and Concurrent Relationships” Journal of Communication 64 612–634 International Communication Association, 2014.
[4] Hong, S., and Nadler, D.” Does the early bird move the polls?: the use of the social media tool’twitter’by us politicians and its impact on public opinion.” In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, ACM, pp. 182–186, 2011.
[5] R. Effing, J. van Hillegersberg, and T. Huibers , “Social Media and Political Participation: Are Facebook, Twitter and YouTube Democratizing Our Political Systems?. “,ePart 2011, LNCS 6847, pp. 25–35, 2011.
[6] Lei Wang and John Q Gan, “Prediction of the 2017 French Election Based on Twitter Data Analysis” , IEEE 2017.
[7] Munaza Ramzan, E. Annapoorna, Shikha Mehta, “Are Tweets The Real Estimators Of Election Results?”, Proceedings of 2017 Tenth International Conference on Contemporary Computing IEEE 2017.
[8] Onur Varol, Emilio Ferrara, Filippo Menczer, Alessandro Flammini, “Early Detection of Promoted Campaigns on Social Media”, arXiv:1703.07518v1 [cs.SI] 22 Mar 2017.
[9] Ben Ltaifa Ibtihela,, Hlaoua Lobnab, Ben Jemaa Maher, “A Semantic Approach for Tweet Categorization”, 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Elsevier 2018.
[10] Parama Fadli Kurnia, Suharjito, “Business Intelligence Model to Analyze Social Media Information”, 3rd International Conference on Computer Science and Computational Intelligence 2018.
[11] Kaplan, A.M., Haenlein, M. “Users of the world, unite! The challenges and oppurtunities of Social Media”. Business Horizons, 53, 59-68. 2010.
[12] Constantinides, E., Fountain, S.J. . “Web 2.0: Conceptual foundations and marketing issues”. Journal of Direct, Data and Digital Marketing Practice, 9(3), 231244. 2008.
[13] Statista , ”U.S. Social Media visit share. Retrieved from Statista”, 2015.
[14] “Social Media KPIs”, measuring your social media and a kpi-cheat sheet, 2014.
[15] Neha S. Joshi, Suhasini A. Itkat, “ A Survey on Feature Level Sentiment Analysis” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014,
[16] Devika M D, Sunitha C, Amal Ganesha, “Sentiment Analysis:A Comparative Study On Different Approaches”, ICRTCSE Elsevier 2016
[17] Trusov, M., Bucklin, R.E., & Pauwels, K., “Monetary Value of word-of-mouth marketing in online communities. Quantifying the Effectiveness of Word-Of-Mouth Marketing”, 2009
[18] Akrimi, Y., & Khemakhem, R. “What Drive Consumers to Spread the Word in Social Media? Journal of Marketing Research & Case Studies, 1-14. “, http://dx.doi.org/10.5171/ 2012.
[19] Tumasjan, A., T. Sprenger, P. Sandner, & I. Welpe, “Predicting elections with Twitter: What 140 characters reveal about political sentiment”. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 178–185, 2010.
[20] Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N., & Malone, T.W., “Evidence for a Collective Intelligence Factor in the Performance of Human Groups.” Science, 330, 686, 2010
[21] Sauter,T, & Bruns, A., “Exploring Emotions on Polarity, Conservativism and Public Performance in the Twitter Debate on Australian Politics”. Selected Papers of Internet Research, 2013.
[22] Aindrila Biswas, Nikhil Ingle and Mousumi Roy, “Influence of Social Media on Voting Behavior”, Journal of Power, Politics & Governance , Vol. 2, No. 2, pp. 127-155, June 2014.
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