Abstract
Due to the advancement of technology, and the promotion of smartphones, using social media got more and more popular. Nowadays, it has become an undeniable part of people’s lives. So, they will create a flow of information by the content they share every single moment. Analyzing this information helps us to have a better understanding of users, their needs, their tendencies and classify them into different groups based on their behavior. These behaviors are various and due to some extracted features, it is possible to categorize the users into different categories. In this paper, we are going to focus on Twitter users and the AusOpen Tennis championship event as a case study. We define the attributions describing each class and then extract data and identify features that are more correlated to each type of user and then label user type based on the reasoning model. The results contain 4 groups of users; Verified accounts, Influencers, Regular profiles, and Fake profiles.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL Student Research Workshop, pp. 88–93, June 2016
Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: TopicSketch: real-time bursty topic detection from twitter. IEEE Trans. Knowl. Data Eng. 28(8), 2216–2229 (2016)
Oh, C., Roumani, Y., Nwankpa, J.K., Hu, H.F.: Beyond likes and tweets: consumer engagement behavior and movie box office in social media. Inf. Manag. 54(1), 25–37 (2017)
El, A., Azab, A.M.I., Mahmoud, M.A., Hefny, H.: Fake account detection in twitter based on minimum weighted feature set. World Acad. Sci. Eng. Technol. Int. J. Comput. Inf. Eng. 10(1) (2016)
Injadat, M., Salo, F., Nassif, A.B.: Data mining techniques in social media: a survey. Neurocomputing 214, 654–670 (2016)
Rashidi, T.H., Abbasi, A., Maghrebi, M., Hasan, S., Waller, T.S.: Exploring the capacity of social media data for modelling travel behaviour: opportunities and challenges. Transp. Res. Part C: Emerg. Technol. 75, 197–211 (2017)
Subrahmanian, V.S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Menczer, F.: The DARPA Twitter bot challenge. Computer 49(6), 38–46 (2016)
Erşahin, B., Aktaş, Ö., Kılınç, D., Akyol, C.: Twitter fake account detection. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 388–392. IEEE, October 2017
Sundararaman, D., Srinivasan, S.: Twigraph: discovering and visualizing influential words between Twitter profiles. In: International Conference on Social Informatics, pp. 329–346. Springer, Cham, September 2017
Crannell, W.C., Clark, E., Jones, C., James, T.A., Moore, J.: A pattern-matched Twitter analysis of US cancer-patient sentiments. J. Surg. Res. 206(2), 536–542 (2016)
Dai, X., Bikdash, M., Meyer, B.: From social media to public health surveillance: word embedding based clustering method for twitter classification. In: SoutheastCon 2017, pp. 1–7. IEEE, March 2017
Kaneko, T., Yanai, K.: Event photo mining from twitter using keyword bursts and image clustering. Neurocomputing 172, 143–158 (2016)
Perez, C., Germon, R.: Graph creation and analysis for linking actors: application to social data. In: Automating Open Source Intelligence, pp. 103–129. Syngress (2016)
Deverashetti, M., Pradhan, S.K.: Identification of topologies by using harmonic centrality in huge social networks. In: 2018 3rd International Conference on Communication and Electronics Systems (ICCES), pp. 443–448. IEEE, October 2018
Bovet, A., Makse, H.A.: Influence of fake news on Twitter during the 2016 US presidential election. Nat. Commun. 10(1), 1–14 (2019)
Gurajala, S., White, J.S., Hudson, B., Matthews, J.N.: Fake Twitter accounts: profile characteristics obtained using an activity-based pattern detection approach. In: Proceedings of the 2015 International Conference on Social Media & Society, pp. 1–7, July 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shoeibi, N., Mateos, A.M., Camacho, A.R., Corchado, J.M. (2021). A Feature Based Approach on Behavior Analysis of the Users on Twitter: A Case Study of AusOpen Tennis Championship. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds) Distributed Computing and Artificial Intelligence, 17th International Conference. DCAI 2020. Advances in Intelligent Systems and Computing, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-53036-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-53036-5_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53035-8
Online ISBN: 978-3-030-53036-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)