Fast Uncovering Of Online Communities Inducing Social Unrest via Non Negative Matrix Factorization
DOI:
https://doi.org/10.53799/skb9d510Keywords:
Non-negative Matrix Factorization, Natural Language Processing, Social Context, Social Network Analysis, Sentiment Analysis, Application Programming InterfaceAbstract
-People are forming social interactions and expressing their thoughts in cyberspace as social networking services like Web 2.0 became more popular. Natural language processing and machine learning techniques, as well as other approaches, working with vast amounts of text, allow for the extraction of attitudes from social platforms. In this paper, we present a novel knowledge-based strategy for detecting online communities that are producing social unrest. The primary motivation towards our goal was to find online user communities that are socially connected and have similar social sentimental contexts. Faced with the threat of social unrest fuelled by social media, our proposed strategy can help the government organisations to ensure the tracking of information flow patterns across social media platforms and the influence of social network data that are fuelling ethnic tensions.References
[1] S. Bashir and M. A. Chachoo, “Social Network Analysis Based Criminal Community Identification Model with Community Structures and Node Attributes,” IEEE Explore, 2022.
[2] M. A. Russell, “Mining the Social Web”, OReilly, 2nd ed. October, 2013.
[3] J. McAuley and J. Leskovec, “Learning to Discover Social Circles in Ego Networks,” NIPS, 2012.
[4] S. Bashir and M. A. Chachoo, “An enforced non-negative matrix factorization based approach towards community detection in dynamic networks”. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 5, pp. doi: 10.17586/2226-1494-2022-22-5-.
[5] D. Lee and H. Seung, “Algorithms for Nonnegative Matrix Factorization,” Adv. Neural Inf. Process. Syst., no. 1, pp. 556–562, 2001.
[6] A. Mukherjee and B. Liu, “Aspect Extraction Through Semi-supervised Modeling,” Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics, 2012.
[7] L. Zhang and B. Liu, “Aspect and Entity Extraction for Opinion Mining,” Data Mining and Knowledge Discovery for Big Data, 2014.
[8] S. Papadopoulos, Y. Kompatsiaris, A. Vakali, and P. Spyridonos, “Community Detection in Social Media,” Data Mining and Knowledge Discovery, 2012.
[9] H. Tajfel, “Social Identity and Intergroup Relations,” Cambridge University Press, 2010.
[10] S. Shankar and Ramaswamy, “Visualization of the Sentiment of the Tweets,” Master's Thesis, North Carolina State University, Raleigh, NC, 2011.
[11] B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and As. Perera, “Opinion Mining and Sentiment Analysis on a Twitter Data Stream,” 2012.
[12] V. N. Patodkar and S. I.R, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” IJARCCE, vol. 5, no. 12, pp. 320–322, Dec, 2016.
[13] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learn-ing techniques,” Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 2002.
[14] N. Malleson and M.A. Andresen, “The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns,” Cartography and Geographic Information Science, 42(2), pp.112–121, 2015.
[15] J. Chan and L.B. Moses, “Is Big Data challenging criminolgy?,” Theoretical Criminology, vol. 20, issue 1, pp. 21-39, 2016.
[16] Y. Wang and Y. Zhang, “Nonnegative Matrix Factorization: AComprehensive Review,” IEEE Trans. Knowl. Data Eng., vol. 25, no.6, pp. 1336–1353, 2013.
[17] A.M. Alghamdi, M. A. Al-Khasawneh, A. Alarood, and E. Alsolami, "The Role of Machine Learning in Managing and Organizing Healthcare Records," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13695–13701, Apr. 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 AIUB Journal of Science and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the work non-commercially provided the original work and source is appropriately cited.
