Abstract
Social media analysis is a fast growing research area aimed at extracting useful information from social media. Several opinion mining techniques have been developed for capturing the mood of social media users related to a specific topic of interest. This paper shows how to use a cloud-based algorithm aimed at discovering the polarization of social media users in relation to political events characterized by the rivalry of different factions. The algorithm has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 Italian constitutional referendum. In particular, Twitter users have been classified and the results have been compared with the polls before voting and with the results obtained after the vote. The achieved results are very close to the real ones.
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References
Ahmed, S., Jaidka, K., Skoric, M.M.: Tweets and votes: a four-country comparison of volumetric and sentiment analysis approaches. In: Tenth International AAAI Conference on Web and Social Media (2016)
Anstead, N., O’Loughlin, B.: Social media analysis and public opinion: the 2010 UK general election. J. Comput. Mediated Commun. 20(2), 204–220 (2014)
Belcastro, L., Cantini, R., Marozzo, F., Talia, D., Trunfio, P.: Discovering political polarization on social media: a case study. In: The 15th International Conference on Semantics, Knowledge and Grids, Guangzhou, China (2019)
Belcastro, L., Marozzo, F., Talia, D., Trunfio, P.: Appraising SPARK on large-scale social media analysis. In: Heras, D.B., Bougé, L. (eds.) Euro-Par 2017. LNCS, vol. 10659, pp. 483–495. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75178-8_39
Belcastro, L., Marozzo, F., Talia, D., Trunfio, P.: Big data analysis on clouds. In: Zomaya, A.Y., Sakr, S. (eds.) Handbook of Big Data Technologies, pp. 101–142. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49340-4_4
Belcastro, L., Marozzo, F., Talia, D., Trunfio, P.: G-RoI: automatic region-of-interest detection driven by geotagged social media data. ACM Trans. Knowl. Discov. Data 12(3), 1–22 (2018)
Belcastro, L., Marozzo, F., Talia, D., Trunfio, P.: ParSoDA: high-level parallel programming for social data mining. Soc. Netw. Anal. Min. 9(1), 1–19 (2018). https://doi.org/10.1007/s13278-018-0547-5
Ceron, A., Curini, L., Iacus, S.M., Porro, G.: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media Soc. 16(2), 340–358 (2014)
El Alaoui, I., Gahi, Y., Messoussi, R., Chaabi, Y., Todoskoff, A., Kobi, A.: A novel adaptable approach for sentiment analysis on big social data. J. Big Data 5(1), 1–18 (2018). https://doi.org/10.1186/s40537-018-0120-0
Graham, T., Jackson, D., Broersma, M.: New platform, old habits? Candidates’ use of Twitter during the 2010 British and Dutch general election campaigns. New Media Soc. 18(5), 765–783 (2016)
Gruzd, A., Roy, J.: Investigating political polarization on Twitter: a canadian perspective. Policy Internet 6(1), 28–45 (2014)
Lee, R., Wakamiya, S., Sumiya, K.: Urban area characterization based on crowd behavioral lifelogs over twitter. Pers. Ubiquit. Comput. 17(4), 605–620 (2013). https://doi.org/10.1007/s00779-012-0510-9
Marozzo, F., Bessi, A.: Analyzing polarization of social media users and news sites during political campaigns. Soc. Netw. Anal. Min. 8(1), 1–13 (2017). https://doi.org/10.1007/s13278-017-0479-5
Oikonomou, L., Tjortjis, C.: A method for predicting the winner of the USA presidential elections using data extracted from Twitter. In: 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA\_CECNSM), pp. 1–8. IEEE (2018)
Olorunnimbe, M.K., Viktor, H.L.: Tweets as a vote: exploring political sentiments on Twitter for opinion mining. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 180–185. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25252-0_19
Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1986)
Acknowledgment
This work has been partially supported by the SMART Project, CUP J28C17000150006, funded by Regione Calabria (POR FESR-FSE 2014–2020) and by the ASPIDE Project funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 801091.
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Belcastro, L., Marozzo, F., Talia, D., Trunfio, P. (2020). Developing a Cloud-Based Algorithm for Analyzing the Polarization of Social Media Users. In: Brandic, I., Genez, T., Pietri, I., Sakellariou, R. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2019. Lecture Notes in Computer Science(), vol 12041. Springer, Cham. https://doi.org/10.1007/978-3-030-58628-7_2
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