Submit your paper : editorIJETjournal@gmail.com Paper Title : A Systematic Review of Predicting Elections Based On Social ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7296028 MLA Style: -Bessy Bijo, D.Raghavi, B.Rishika, G.Gayathri A Systematic Review of Predicting Elections Based On Social , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Bessy Bijo, D.Raghavi, B.Rishika, G.Gayathri A Systematic Review of Predicting Elections Based On Social , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract The way politicians communicate with the electorate and run electoral campaigns was reshaped by the emergence and popularization of contemporary socialmedia (SM), such as Facebook, Twitter, and Instagram social networks (SNs). Due to the inherent capabilities ofSM, such as the large amount of available data accessed on using the SM data to predict election outcomes. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results withnew approaches, such as regression methods trained withtraditional polls. Finally, a vision of future research on integrating advances in process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application ofstate-of the-art machine learning approaches. Reference . Jungherr, “Twitter use in election campaigns: A systematic literature review,” J. Inf. Technol. Politics, vol. 13, no. 1, pp. 72–91, Jan. 2016. P. L. Francia, “Free media and Twitter in the 2016 presidential election: The unconventional of Trump,” Social Sci. Computer. Rev., vol. 36, no. 4, pp. 440–455, Aug. 2018. K. Brito, N. Paula, M. Fernandes, and S. Meira, “Social media and presidential campaigns– preliminary results of the 2018 Brazilian presidential election,” in Proc. 20th Annu. Int. Conf. Digit. Government Res., Jun. 2019, pp. 332–341. S. Tilton, “Virtual polling data: A social network analysis on a student government election,” Webology, vol. 5, no. 4, pp. 1–8, 2008. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Predicting elections with Twitter: What 140 characters reveal about political sentiment,” in Proc. 4th Int. AAAI Conf. Weblogs SocialMedia, 2010, pp. 1–8. A. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, “From tweets to polls: Linking text sentiment to public opinion time series,” in Proc. 4th Int. AAAI Conf. Weblogs SocialMedia, 2010,pp. 1–8. E. Sang and J. Bos, “Predicting the 2011 Dutch senate election results with Twitter,” in Proc. Workshop Semantic Anal. Social Media, 2012, pp. 53–60. A. Ceron, L. Curini, S. M. Iacus, and G. Porro, “Every tweet counts? How sentiment analysis of socialmedia can improve our knowledge of citizens’ political preferences with an application to Italy andFrance,” New Media Soc., vol. 16, no. 2, pp. 340–358, Mar. 2014. K. Singhal, B. Agrawal, and N. Mittal, “Modeling Indian general elections: Sentiment analysis of political Twitter data,” in Information Systems Design and Intelligent Applications (Advances in Intelligent Systems and Computing). New Delhi, India: Springer, 2015. N. DwiPrasetyo and C. Hauff, “Twitter-based election prediction in the developing world,” in Proc. 26th ACM Conf. Hypertext Social Media (HT), 2015, pp. 149–158. J. A. Ceron-Guzman and E. Leon-Guzman, “A sentiment analysis system of Spanish tweets and its application in Colombia 2014 presidential election,” in Proc. IEEE Int. Conf. Big Data Cloud Computer. (BDCloud), Social Computer. Netw. (SocialCom), Sustain. Computer. Communication. (SustainCom) (BDCloud-Social Com-Sustain Com), Oct. 2016, pp. 250– Keywords — A Systematic Review of Predicting Elections Based On Social |