Abstract
Predicting popularity from social media has been explored about a decade. As far as the number of social media users is soaring, understanding the relationship between popularity and social media is really beneficial because it can be mapped to the real popularity of an entity. The popularity in social media, for instance in Twitter, is interpreted by drawing a relationship between a social media account and its followers. Therefore, in this paper, to understand the popularity of candidates of the US election 2016 in social media, we verify this association in Twitter by analyzing the candidates’ tweets. More specifically, our aim is to assess if candidates put efforts to improve their style of tweeting over time to be more favorable to their followers. We show that Mr. Trump could wisely exploit Twitter to attract more people by tweeting in a well-organized and desirable manner and that tweeting style has increased his popularity in social media.
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See, for example: http://www.snopes.com/donald-trump-sentence/.
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References
Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 01, pp. 492–499. IEEE Computer Society, Washington, DC (2010). doi:10.1109/WI-IAT.2010.63
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING 2010, pp. 36–44. Association for Computational Linguistics, Stroudsburg, PA, USA (2010). http://dl.acm.org/citation.cfm?id=1944566.1944571
Bradshaw, S., Howard, P.N.: Troops, trolls and troublemakers: a global inventory of organized social media manipulation (2017). http://comprop.oii.ox.ac.uk/wp-content/uploads/sites/89/2017/07/Troops-Trolls-and-Troublemakers.pdf. Working paper
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). doi:10.1023/A:1010933404324
Emanet, N., Öz, H.R., Bayram, N., Delen, D.: A comparative analysis of machine learning methods for classification type decision problems in healthcare. Decis. Analytics 1(1), 6 (2014). doi:10.1186/2193-8636-1-6
Eysenbach, G.: Can tweets predict citations? metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. J. Med. Internet. Res. 13, e123 (2011)
Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014). http://dl.acm.org/citation.cfm?id=2627435.2697065
Gayo-Avello, D.: Don’t turn social media into another ‘literary digest’ poll. Commun. ACM 54(10), 121–128 (2011). doi:10.1145/2001269.2001297
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2003). http://www.worldcat.org/isbn/0387952845
Henrique, J.: Getoldtweets by python. https://github.com/Jefferson-Henrique/GetOldTweets-python
Jahanbakhsh, K., Moon, Y.: The predictive power of social media: On the predictability of U.S. presidential elections using twitter. CoRR abs/1407.0622 (2014). http://arxiv.org/abs/1407.0622
Jarvis, S.E.: Communicator-in-chief: How barack obama used new media technology to win the white house edited by John Allen Hendricks and Robert Denton Jr. Presidential Stud. Q. 40(4), 800–802 (2010). doi:10.1111/j.1741-5705.2010.03815.x
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 151–160. Association for Computational Linguistics, Stroudsburg, PA, USA (2011). http://dl.acm.org/citation.cfm?id=2002472.2002492
Kupavskii, A., Ostroumova, L., Umnov, A., Usachev, S., Serdyukov, P., Gusev, G., Kustarev, A.: Prediction of retweet cascade size over time. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, NY, USA, pp. 2335–2338 (2012). doi:10.1145/2396761.2398634
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002). http://CRAN.R-project.org/doc/Rnews/
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Näppi, J.J., Regge, D., Yoshida, H.: Comparative performance of random forest and support vector machine classifiers for detection of colorectal lesions in CT colonography. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) ABD-MICCAI 2011. LNCS, vol. 7029, pp. 27–34. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28557-8_4
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, 23–26 May 2010 (2010)
Oliver, J.E., Rahn, W.M.: Rise of the trumpenvolk. Ann. Am. Acad. Polit. Soc. Sci. 667(1), 189–206 (2016). doi:10.1177/0002716216662639
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Chair, N.C.C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association (ELRA), Valletta, Malta, May 2010
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008). doi:10.1561/1500000011
Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! predicting message propagation in twitter. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, 17–21 July 2011 (2011)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: Proceedings of the 2010 IEEE Second International Conference on Social Computing, SOCIALCOM 2010, pp. 177–184 (2010). doi:10.1109/SocialCom.2010.33
Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185 (2010)
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, ACL 2012, pp. 115–120. Association for Computational Linguistics, Stroudsburg, PA, USA (2012). http://dl.acm.org/citation.cfm?id=2390470.2390490
Zaman, T., Fox, E.B., Bradlow, E.T.: A bayesian approach for predicting the popularity of tweets. CoRR abs/1304.6777 (2013). http://arxiv.org/abs/1304.6777
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Tavazoee, F., Conversano, C., Mola, F. (2017). Investigating the Relationship Between Tweeting Style and Popularity: The Case of US Presidential Election 2016. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_9
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