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Sherchan, W., Nepal, S., Paris, C.: A survey of trust in social networks. ACM Comput. Surv. 45(4), 47–14733 (2013). https://doi.org/10.1145/2501654.2501661 Cercel, D.-C., Trausan-Matu, S.: Opinion propagation in online social networks: a survey. ACM International Conference Proceeding Series (2014). https://doi.org/10.1145/2611040.2611088 Allor, M.: Relocating the site of the audience. Crit. Stud. Mass Commun. 5(3), 217–233 (1988). https://doi.org/10.1080/15295038809366704 Reynolds, W.N., Salter, W.J., Farber, R.M., Corley, C., Dowling, C.P., Beeman, W.O., Smith-Lovin, L., Choi, J.N.: Sociolect-based community detection. In: 2013 IEEE International Conference on Intelligence and Security Informatics, pp. 221-226 (2013). https://doi.org/10.1109/ISI.2013.6578823 Golbeck, J.: Trust and nuanced profile similarity in online social networks. ACM Trans. Web 3(4), 12–11233 (2009). https://doi.org/10.1145/1594173.1594174 Mansouri, F., Abdelalim, S., Ikram, E.A.: A modeling framework for the moroccan sociolect recognition used on the social media. In: Proceedings of the 2Nd International Conference on Big Data, Cloud and Applications. BDCA’17, pp. 34–1345. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3090354.3090389 Zanzotto, F.M., Pennacchiotti, M., Tsioutsiouliklis, K.: Linguistic redundancy in twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. EMNLP ’11, pp. 659–669. Association for Computational Linguistics, Stroudsburg, PA, USA (2011). http://dl.acm.org/citation.cfm?id=2145432.2145509 Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E.P., Ungar, L.H.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9), 73791 (2013). https://doi.org/10.1371/journal.pone.0073791 Yang, Y., Eisenstein, J.: Putting things in context: community-specific embedding projections for sentiment analysis (2015) Rampton, B., Tusting, K., Maybin, J., Barwell, R.D.: UK linguistic ethnography: a discussion paper coordinating committee UK linguistic ethnography forum 1, (2004) Rangel, F.M., Rosso, P., Montes-yGómez, M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at pan 2018: multimodal gender identification in twitter. In: Notes Papers of the CLEF (2018) Moreno-Sandoval, L.G., Puertas, E.A., Plaza-del-Arco, F.M., Pomares-Quimbaya, A., Alvarado-Valencia, J.A., Alfonso, L., Ureña-López: Celebrity profiling on twitter using sociolinguistic features notebook for pan at clef 2019. (2019) Phad, P.V., Chavan, M.K.: Detecting compromised high-profile accounts on social networks. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–4 (2018). https://doi.org/10.1109/ICCCNT.2018.8493851 Singh, M., Bansal, D., Sofat, S.: Who is who on twitter–spammer, fake or compromised account? A tool to reveal true identity in real-time. Cybern. Syst. 49(1), 1–25 (2018). https://doi.org/10.1080/01969722.2017.1412866 Aggarwal, C.C.. In: Aggarwal, C.C. (ed.): An Introduction to Social Network Data Analytics, pp. 1–15. Springer, Boston, MA (2011). https://doi.org/10.1007/978-1-4419-8462-3_1 Scott, J.: Social network analysis: developments, advances, and prospects. Soc. Netw. Anal. Min. 1(1), 21–26 (2011). https://doi.org/10.1007/s13278-010-0012-6 Vatrapu, R., Mukkamala, R.R., Hussain, A., Flesch, B.: Social set analysis: a set theoretical approach to big data analytics. IEEE Access 4, 1–1 (2016). https://doi.org/10.1109/ACCESS.2016.2559584 Li, C., Bai, J., Zhang, L., Tang, H., Luo, Y.: Opinion community detection and opinion leader detection based on text information and network topology in cloud environment. Inf. Sci. 504, 61–83 (2019). https://doi.org/10.1016/j.ins.2019.06.060 Zhang, H., Nguyen, D., Zhang, H., Thai, M.: Least cost influence maximization across multiple social networks. IEEE/ACM Trans. Netw. 24, 1–11 (2015). https://doi.org/10.1109/TNET.2015.2394793 Jadhav, K.U., Mhetre, N.A.: Mass users behaviour prediction in social media: a survey. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5, 3286–3288 (2014) Fan, L., Wu, W., Zhai, X., Xing, K., Lee, W., Du, D.-Z.: Maximizing rumor containment in social networks with constrained time. Soc. Netw. Anal. Min. (2014). https://doi.org/10.1007/s13278-014-0214-4 Nguyen, D., Doğruöz, A.S., Rosé, C.P., de Jong, F.: Computational sociolinguistics: a survey. Comput. Linguist. 42(3), 537–593 (2016). https://doi.org/10.1162/COLI_a_00258 Tsytsarau, M., Palpanas, T.: Survey on mining subjective data on the web. Data Min. Knowl. Discov. 24(3), 478–514 (2012). https://doi.org/10.1007/s10618-011-0238-6 Radivchev, V., Nikolov, A., Lambova, A.: Celebrity profiling using tf-idf, logistic regression, and svm—notebook for pan at clef 2019. In: Cappellato, L., Ferro, N., Losada, D.E., Müller, H. (eds.) CLEF 2019 Labs and Workshops, Notebook Papers, vol. 2380. CEUR-WS.org, Switzerland (2019). http://ceur-ws.org/Vol-2380/ Martinc, M., Škrlj, B., Pollak, S.: Who is hot and who is not? Profiling celebs on Twitter—notebook for PAN at CLEF 2019. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) CLEF 2019 Labs and Workshops, Notebook Papers, vol. 2380. CEUR-WS.org, Switzerland (2019). http://ceur-ws.org/Vol-2380/ Petrik, J., Chuda, D.: Twitter feeds profiling with TF-IDF—notebook for PAN at CLEF 2019. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) CLEF 2019 Labs and Workshops, Notebook Papers, vol. 2380. CEUR-WS.org, Switzerland (2019). http://ceur-ws.org/Vol-2380/ Simaki, V., Aravantinou, C., Mporas, I., Kondyli, M., Megalooikonomou, V.: Sociolinguistic features for author gender identification: from qualitative evidence to quantitative analysis. J. Quant. Linguist. 24(1), 65–84 (2017). https://doi.org/10.1080/09296174.2016.1226430 Peersman, C., Daelemans, W., Van Vaerenbergh, L.: Predicting age and gender in online social networks. In: Proceedings of the 3rd International Workshop on Search and Mining User-generated Contents. SMUC ’11, pp. 37–44. , ACM, New York, NY, USA (2011). https://doi.org/10.1145/2065023.2065035 Huang, Y., Yu, L., Wang, X., Cui, B.: A multi-source integration framework for user occupation inference in social media systems. World Wide Web 18(5), 1247–1267 (2015). https://doi.org/10.1007/s11280-014-0300-6 Sánchez-Rebollo, C., Puente, C., Palacios, R., Piriz, C., Fuentes, J.P., Jarauta, J.: Detection of jihadism in social networks using big data techniques supported by graphs and fuzzy clustering. Complexity 2019, 1–13 (2019). https://doi.org/10.1155/2019/1238780 Milroy, J., Milroy, L.: Mechanisms of change in urban dialects: the role of class, social network and gender. Int. J. Appl. Linguist. 3(1), 57–77 (1993). https://doi.org/10.1111/j.1473-4192.1993.tb00043.x Przybyła, P., Teisseyre, P.: Analysing utterances in polish parliament to predict speaker’s background. J. Quant. Linguist. 21(4), 350–376 (2014) Argamon, S., Fine, J., Rachel Shimoni, A.: Gender, genre, and writing style in formal written texts. Text (2003). https://doi.org/10.1515/text.2003.014 Romaine, S.: Language and Social Class, pp. 281–287. (2015). https://doi.org/10.1016/B978-0-08-097086-8.53015-3 Sloan, L., Morgan, J., Burnap, P., Williams, M.: Who tweets? deriving the demographic characteristics of age, occupation and social class from twitter user meta-data. PLOS ONE 10(3), 1–20 (2015). https://doi.org/10.1371/journal.pone.0115545 Wiegmann, M., Stein, B., Potthast, M.: Celebrity profiling. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2611–2618. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1249 Watts, D., Dodds, P.: Influentials, networks, and public opinion formation. J. Consum. Res. 34, 441–458 (2007). https://doi.org/10.1086/518527 Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (2007). https://doi.org/10.1145/1232722.1232727 Djafarova, E., Trofimenko, O.: ‘instafamous’—credibility and self-presentation of micro-celebrities on social media. Inf. Commun. Soc. 22(10), 1432–1446 (2019) Wang, Y.-C., Kraut, R.E.: Twitter and the development of an audience: those who stay on topic thrive! In: CHI (2012) Hutto, C.J., Yardi, S., Gilbert, E.: In: A longitudinal study of follow predictors on twitter, In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’13, pp. 821–830. , ACM, New York, NY, USA (2013). https://doi.org/10.1145/2470654.2470771 Chang, S., Kumar, V., Gilbert, E., Terveen, L.: Specialization, homophily, and gender in a social curation site: Findings From Pinterest, pp. 674–686 (2014). https://doi.org/10.1145/2531602.2531660 Wang, Chun: Ya Jun Du, Ming Wei Tang: Opinion leader mining algorithm in microblog platform based on topic similarity. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 160-165 (2016). https://doi.org/10.1109/CompComm.2016.7924685 Kiang, M.Y.: Neural networks. In: Bidgoli, H. (ed.) Encyclopedia of Information Systems, pp. 303–315. Elsevier, New York (2003). https://doi.org/10.1016/B0-12-227240-4/00121-0 . https://www.sciencedirect.com/science/article/pii/B978008044910400482X Casas, I.: Neural networks. In: Kitchin, R., Thrift, N. (eds.) International Encyclopedia of Human Geography, pp. 419–422. Elsevier, Oxford (2009). https://doi.org/10.1016/B978-008044910-4.00482-X . www.sciencedirect.com/science/article/pii/B978008044910400482X Hsu, C.-C., Lee, Y.-C., Lu, P.-E., Lu, S.-S., Lai, H.-T., Huang, C.-C., Wang, C., Lin, Y.-J., Su, W.-T.: Social media prediction based on residual learning and random forest, In: Proceedings of the 25th ACM International Conference on Multimedia. MM ’17, pp. 1865-1870. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3123266.3127894 Huang, J., Tang, Y., Hu, Y., Li, J., Hu, C.: Predicting the active period of popularity evolution: a case study on twitter hashtags. Inf. Sci. 512, 315–326 (2020). https://doi.org/10.1016/j.ins.2019.04.028 Zhang, Q., Gong, Y., Wu, J., Huang, H., Huang, X.: In: Retweet prediction with attention-based deep neural network. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. CIKM ’16, pp. 75-84. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2983323.2983809 Li, J., Xu, H., He, X., Deng, J., Sun, X.: Tweet modeling with lstm recurrent neural networks for hashtag recommendation, pp. 1570–1577 (2016). https://doi.org/10.1109/IJCNN.2016.7727385 Simaki, V., Mporas, I., Megalooikonomou, V.: Evaluation and sociolinguistic analysis of text features for gender and age identification. Am. J. Eng. Appl. Sci. 9, 868–876 (2016). https://doi.org/10.3844/ajeassp.2016.868.876 Johannsen, A., Hovy, D., Søgaard, A.: Cross-lingual syntactic variation over age and gender. (2015). https://doi.org/10.18653/v1/K15-1011 Namugera, F., Wesonga, R., Jehopio, P.: Text mining and determinants of sentiments: Twitter social media usage by traditional media houses in Uganda. Comput. Soc. Netw. (2019). https://doi.org/10.1186/s40649-019-0063-4 Zhong, G., Wang, L.-N., Dong, J.: An overview on data representation learning: from traditional feature learning to recent deep learning. J. Financ. Data Sci. (2016). https://doi.org/10.1016/j.jfds.2017.05.001 Wan, Y., Chen, X., Zhang, J.: Global and intrinsic geometric structure embedding for unsupervised feature selection. Expert Syst. Appl. (2017). https://doi.org/10.1016/j.eswa.2017.10.008 Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A Opt Image Sci. 4, 519–24 (1987). https://doi.org/10.1364/JOSAA.4.000519 Jolliffe, I.. In: Lovric, M. (ed.) Principal Component Analysis, pp. 1094–1096. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2_455 Peng, H., Bao, M., Li, J., Bhuiyan, M., Liu, Y., He, Y., Yang, E.: Incremental term representation learning for social network analysis. Future Gener. Comput. Syst. 86, 1503–1512 (2018). https://doi.org/10.1016/j.future.2017.05.020 Wang, S., Tang, J., Liu, H.: Embedded unsupervised feature selection. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI’15, pp. 470-476. AAAI Press. (2015) Zhang, B., Xiang, J., Wang, X.: Network representation learning with ensemble methods. Neurocomputing 380, 141–149 (2020). https://doi.org/10.1016/j.neucom.2019.10.098 Peña, D.: Análisis de Datos Multivariantes. S.A. MCGRAW-HILL / INTERAMERICANA DE ESPAÑA, España (2002) Sluban, B., Smailović, J., Battiston, S., Mozetič, I.: Sentiment leaning of influential communities in social networks. Comput. Soc. Netw. (2015). https://doi.org/10.1186/s40649-015-0016-5 Avnit, A.: The million followers fallacy. Pravda Media Group (2009) Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE Second International Conference on Social Computing, pp. 177-184 (2010) Potthast, M., Gollub, T., Wiegmann, M., Stein, B.: TIRA Integrated Research Architecture, pp. 123–160 (2019). https://doi.org/10.1007/978-3-030-22948-1_5 Yazdanfar, N., Thomo, A.: Link recommender: Collaborative-filtering for recommending urls to twitter users. Procedia Computer Science 19, 412–419 (2013). https://doi.org/10.1016/j.procs.2013.06.056. The 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013), the 3rd International Conference on Sustainable Energy Information Technology (SEIT-2013) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011) Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017) Wiegmann, M., Stein, B., Potthast, M.: Overview of the Celebrity Profiling Task at PAN 2019. In: Cappellato, L., Ferro, N., Losada, D., Müller, H. (eds.) CLEF 2019 Labs and Workshops, Notebook Papers, vol. 2380. CEUR-WS.org, Switzerland (2019). http://ceur-ws.org/Vol-2380/ Lim, K.H., Datta, A.: Finding twitter communities with common interests using following links of celebrities. (2012). https://doi.org/10.1145/2310057.2310064 Stoop, W., Van den Bosch, A.: Using idiolects and sociolects to improve word prediction, pp. 318–327 (2014). https://doi.org/10.3115/v1/E14-1034 Copland, F., Shaw, S., Snell, J.: Linguistic Ethnography: Interdisciplinary Explorations. Springer, London (2016) Choi, C.J., Berger, R.: Ethics of celebrities and their increasing influence in 21st century society. J. Bus. Ethics 91(3), 313–318 (2010). https://doi.org/10.1007/s10551-009-0090-4 Friendly, M.: Corrgrams: exploratory displays for correlation matrices. Am. Stat. 56, 316–324 (2002) Chessel, D., Dufour, A.-B., Thioulouse, J.: The ade4 package - I: one-table methods. R News 4(1), 5–10 (2004) Lê, S., Josse, J., Husson, F.: FactoMineR: an R package for multivariate analysis. J. Stat. Softw. Artic. 25(1), 1–18 (2008). https://doi.org/10.18637/jss.v025.i01 Cappellato, L., Ferro, N., Losada, D.E., Müller, H. (eds.): CLEF 2019 Labs and Workshops, Notebook Papers, vol. 2380. CEUR-WS.org, Switzerland (2019) Moreno-Sandoval, L.G., Mendoza-Molina, J.F., Puertas-Del Castillo, E.A., Duque-Marín, A., Pomares-Quimbaya, A., Alvarado-Valencia, J.A.: Age classification from Spanish tweets - the variable age analyzed by using linear classifiers. In: Hammoudi, S., Smialek, M., Camp, O., Filipe, J. (eds.) Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pp. 275–281 (2018). https://doi.org/10.5220/0006811102750281 Moreno-Sandoval, L.G., Sanchéz-Barriga, C., Espíndola-Buitrago, K., Pomares-Quimbaya, A., Garcia, G.C.: Spanish Twitter data used as a source of information about consumer food choice. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds.) Machine Learning and Knowledge Extraction. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. CD-MAKE 2018. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99740-7_9