Abstract
The paper studies the community detection problem on Telegram channels. The dataset is received from TGStat service and includes the information of 58k forwards between 100 politician Telegram channels. We implement modern clustering approaches to solve the problem of missing social links. Our study is based on a combination of structural features with strategy-based attributes, including indicators designed according to the nodes’ role in a network. Authors provide ten novel indicators, which are calculated for each network’s member per each message in order to vectorize a Telegram channel with regard to its strategy of information spread and the way of contacting other channels. Authors construct a metric-based graph of channel relations and cluster channels representations using network science techniques. Obtained results are studied using quantitative and qualitative analysis showing promising results in applying joint network-based and KPI-based models for the stated problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Benson, A., Kleinberg, J.: Link prediction in networks with core-fringe data. In: Proceedings of the World Wide Web Conference, WWW 2019, pp. 94–104, (2019)
Castells, M.: The Power of Communication. Publishing House of the Higher School of Economics, Moscow (2009)
Chin-Fook, L., Simmonds, H.: Redefining gatekeeping theory for a digital generation redefining gatekeeping theory for a digital generation. McMaster J. Commun. 8, 7–34 (2011)
Flache, A., et al.: Models of social influence: towards the next frontiers. J. Artif. Soc. Soc. Simul. 20(4), 1–31 (2017)
Keen, A.: The Cult of the Amateur: How Blogs, Myspace, Youtube, and the Rest of Today’s User-Generated Media are Destroying Our Economy, Our Culture, and Our Values. Doubleday, New York (2008)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. JMLR 12, 2825–2830 (2011)
Prokhorenkova, L., Tikhonov, A., Litvak, N.: Learning clusters through information diffusion. In: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 3151–3157 (2019)
Prokhorenkova, L., Tikhonov, A.: Community detection through likelihood optimization: in search of a sound model. In: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 1498–1508 (2019)
Sunstein, C.R.: # Republic: Divided Democracy in the Age of Social Media. Princeton University Press, Princeton (2018)
Vos, T.P., Russell, F.: Theorizing journalism’s institutional relationships: an elaboration of gatekeeping theory. J. Stud. 20(16), 2331–2348 (2019)
Wu, T., Chen, L., Xian, X., Guo, Y.: Evolution prediction in multi-scale information diffusion dynamics. Knowl.-Based Syst. 113, 186–198 (2016)
Zhu, H., Yin, X., Ma, J., Hu, W.: Identifying the main paths of information diffusion in online social networks. Physica A 452, 320–328 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tikhomirova, K., Makarov, I. (2021). Community Detection Based on the Nodes Role in a Network: The Telegram Platform Case. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-72610-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72609-6
Online ISBN: 978-3-030-72610-2
eBook Packages: Computer ScienceComputer Science (R0)