Analyses of Methods for Prediction of Elections Using Software Systems
Keywords:
machine learning, social networks, election prediction, methods, twitterAbstract
The primary objective of this research study is to review and analyze the published literature regarding the possibilities of forecasting and predicting the result of elections using software systems. The factors motivating research institutions and individuals to consider research impact on prediction of elections are manifold. Understanding the impact of different software tools, algorithms and social networking software applications on prediction of elections is a vital, and often overlooked, element of forecasting the election results. The literature review was conducted to examine methods and current software applications and practices as well as projects on election predictions. The review focused in particular on social media applications and different methods on accessing the opinion of the potential voters. The review draws on an international literature, although it is limited to English language publications. The findings identify the different methods used, the advantages and disadvantages of different approaches and the methods that are used currently and that have shown most effective results and recommendations are provided.
References
. Gayo-Avello, D.: No, you cannot predict elections with twitter. IEEE Internet Computing 16(6), 91{94 (2012). doi:10.1109/MIC.2012.137
. Garcia, A.C.B., Silva, W., Correia, L.: The PredNews forecasting model. In: Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age. Association for Computing Machinery, New York, NY, USA (2018). doi:10.1145/3209281.3209295
. Gayo-Avello, D.: Analyzes predicting elections with twitter. IEEE Internet Computing 16(5), (2012). doi:10.1109/MIC.2012.142
. Gayo-Avello, D.: A meta-analysis of state-of-the-art electoral prediction from twitter data. Soc. Sci. Comput. Rev. 31(6), 649{679 (2013). doi:10.1177/0894439313493979
. Grimaldi, D., Cely, J.D., Arboleda, H.: Inferring the votes in a new political landscape: the case of the 2019 Spanish presidential elections. J Big Data 7 (2020). doi:10.1186/s40537-020-00334-5
. Hassin, Y., Peleg, D.: Distributed probabilistic polling and applications to proportionate agreement. Inf. Comput. 171(2), 248{268 (2002). doi:10.1006/inco.2001.3088
. Holley, R.A., Liggett, T.M.: Ergodic theorems for weakly interacting infinite systems and the voter model. Ann. Probab. 3(4), 643{663 (1975). doi:10.1214/aop/1176996306
. Huberty, M.E.: Multi-cycle forecasting of congressional elections with social media. In: Proceedings of the 2nd Workshop on Politics, Elections and Data. PLEAD ’13, pp. 23{30. Association for Computing Machinery, New York, NY, USA (2013). doi:10.1145/2508436.2508439
. Jungherr, A., J¨urgens, P., Schoen, H.: Why the pirate party won the Brazilian election of 2009 or the trouble with predictions: A response to tumasjan, a., sprenger, t. o., sander, p. g., & welpe, i. m. predicting elections with twitter: What 140 characters reveal about political sentiment". Soc. Sci. Comput. Rev. 30(2), 229{234 (2012). doi:10.1177/0894439311404119
. Jungherr, A., Schoen, H., Posegga, O., J¨urgens, P.: Digital trace data in the study of public opinion: An indicator of attention toward politics rather than political support. Social Science Computer Review 35(3), 336{356 (2017). doi:10.1177/0894439316631043
. Klamser, P.P., Wiedermann, M., Donges, J.F., Donner, R.V.: Zealotry effects on opinion dynamics in the adaptive voter model. Phys. Rev. E 96 (2017). doi:10.1103/PhysRevE.96.052315
. Jungherr, A., Schoen, H., Posegga, O., J¨urgens, P.: Digital trace data in the study of public opinion: An indicator of attention toward politics rather than political support. Social Science Computer Review 35(3), 336{356 (2017). doi:10.1177/0894439316631043
. Mobilia, M.: Does a single zealot affect an infinite group of voters? Phys. Rev. Lett. 91, 028701 (2003). doi:10.1103/PhysRevLett.91.028701
. Audickas, L., Cracknell, R., Loft, P.: UK Election Statistics: 1918-2019 { A Century of Elections (2020). https://commonslibrary.parliament.uk/research-briefings/cbp-7529/
. Bovet, A., Morone, F., Makse, H.A.: Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump. Sci. Rep. 8, 8673 (2018). doi:10.1038/s41598-018-26951-y
. Caldarelli, G., Chessa, A., Pammolli, F., Pompa, G., Puliga, M., Riccaboni, M., Riotta, G.: A multi-level geographical study of italian political elections from twitter data. PLoS One 9(5), 1{11 (2014). doi:10.1371/journal.pone.0095809
. Chen, P., Redner, S.: Majority rule dynamics in finite dimensions. Phys. Rev. E 71 (2005). doi:10.1103/PhysRevE.71.036101
. Clifford, P., Sudbury, A.: A model for spatial conflict. Biometrika 60(3), 581{588 (1973). doi:10.1093/biomet/60.3.581
. Dokoohaki, N., Zikou, F., Gillblad, D., Matskin, M.: Predicting swedish elections with twitter: A case for stochastic link structure analysis. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1269{1276 (2015). doi:10.1145/2808797.2808915
. Fernley, J., Ortgiese, M.: Voter models on subcritical inhomogeneous random graphs (2019). 1911.13187 8. Fink, C., Bos, N., Perrone, A., Liu, E., Kopecky, J.: Twitter, public opinion, and the 2011 Nigerian presidential election. In: 2013 International Conference on Social Computing, pp. 311{320 (2013). doi:10.1109/SocialCom.2013.50
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
