Abstract
The emergence of a networked social structure in the last decade of twentieth century is accelerated by the evolution of information technologies and, in particular, the Internet has given rise to the full emergence of what has been called the Information Age [1] or the Information Society [2]. Social media is yet another example of people’s extraordinary ability to generate, disseminate and exchange meanings in collective interpersonal communication with a massive, real-time networked system where everything tends to be connected. The analysis of the climate of opinion on Twitter is presented around the Common Core State Standards (CCSS), one of the most ambitious educational reforms of the last 50 years in USA.
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References
Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)
Yuan, M., Ouyang, Y., Xiong, Z., Sheng, H.: Sentiment classification of web review using association rules. In: Ozok, A.A., Zaphiris P. (eds.) Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol. 8029. Springer, Berlin, Heidelberg (2013)
Silverstein, C., Brin, S., Motwani, R., Ullman, J.: Scalable techniques for mining causal structures. Data Mining Knowl. Discov. 4(2–3), 163–192 (2000)
Amelec, V., Carmen, V.: Relationship between variables of performance social and financial of microfinance institutions. Adv. Sci. Lett. 21(6), 1931–1934 (2015)
Java, A., Finin, T., Song, X., Tseng, B.: Why we Twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD’07 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis (2007)
Naaman, M., Boase, J., Lai, C.-H.: Is it really about me? Message content in social awareness streams. In: CSCW’10 Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work (2010)
Zafarani, R., Ali Abbasi, M., Liu, H.: Social Media Mining. Cambridge University Press. http://dx.doi.org/10.1017/cbo9781139088510 (2014)
Morris, M.R., Teevan, J., Panovich, K.: What do people ask their social networks, and why? A survey study of status message Q&A behavior. In: CHI’10 Proceedings of the 28th International Conference on Human Factors in Computing Systems (2010)
Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Proc. Comput. Sci. 151, 1201–1206 (2019)
Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernandez, L., Cali, E.G.: Database performance tuning and query optimization. In: International Conference on Data Mining and Big Data, pp. 3–11. Springer, Cham (2018)
Mayer-Schönberger, V., Cukier, K.: Big data: a revolution that will transform how we live, work, and think. Mariner Books. http://dx.doi.org/10.2501/ija-33-1-181-183 (2014)
Cagliero, L., Fiori, A.: Analyzing Twitter user behaviors and topic trends by exploiting dynamic rules. In: Behavior Computing: Modeling, Analysis, Mining and Decision, pp. 267–287. Springer (2012)
Erlandsson, F., Brodka, P., Borg, A., Johnson, H.: Finding influential users in social media using association rule learning. Entropy 18, 164 (2016)
Meduru, M., Mahimkar, A., Subramanian, K., Padiya, P.Y., Gunjgur, P.N.: Opinion mining using twitter feeds for political analysis. Int. J. Comput. (IJC) 25(1), 116–123 (2017)
Abascal-Mena, R., López-Ornelas, E., Zepeda-Hernández, J.S.: User generated content: an analysis of user behavior by mining political tweets. In: Ozok, A.A., Zaphiris, P. (eds.) Online Communities and Social Computing. OCSC 2013. Lecture Notes in Computer Science, vol. 8029. Springer, Berlin, Heidelberg
Dehkharghani, R., Mercan, H., Javeed, A., Saygin, Y.: Sentimental causal rule discovery from Twitter. Expert Syst. Appl. 41(10), 4950–4958 (2014)
Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363–370 (2005)
Viloria, A., et al.: Integration of data mining techniques to PostgreSQL database manager system. Proc. Comput. Sci. 155, 575–580 (2019)
Guandong, X., Lin, L., (eds.). (2013). Social media mining and social network analysis; emerging research. Reference and Research Book News 28(3). Portland: Ringgold Inc. http://dx.doi.org/10.4018/978-1-4666-2806-9
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Silva, J. et al. (2020). Data Mining and Social Network Analysis on Twitter. In: Bindhu, V., Chen, J., Tavares, J. (eds) International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, vol 637. Springer, Singapore. https://doi.org/10.1007/978-981-15-2612-1_39
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