Uncovering Hidden Patterns in Social Media Data Using Advanced Network Analytics and Sentiment Analysis
Keywords:
Social Media Analytics, Network Science, Sentiment Analysis, Twitter Data, Community Detection, Emotion Mining, Transformer Models, Hidden PatternsAbstract
In the digital era, social media platforms have emerged as critical spaces for public discourse, marketing, and socio-political mobilization. This study explores the intersection of network analytics and sentiment analysis to uncover latent structures and emotional trends in large-scale social media datasets. By integrating graph-based community detection with transformer-based sentiment classification models, the paper presents a comprehensive methodology to reveal underlying patterns that are not readily visible through traditional metrics. Empirical analysis was conducted using Twitter data collected during the 2024 Indian general elections, uncovering polarized communities and significant temporal sentiment shifts. The findings have implications for public opinion modeling, misinformation detection, and social network research.
References
Bail, Christopher A., et al. "Exposure to Opposing Views on Social Media Can Increase Political Polarization." Proceedings of the National Academy of Sciences, vol. 115, no. 37, 2018, pp. 9216–9221.
Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of NAACL-HLT, 2019.
Gruhl, Daniel, et al. "Information Diffusion Through Blogspace." Proceedings of the 13th International Conference on World Wide Web (WWW), 2004, pp. 491–501.
Kumar, Shubhanshu, and Kathleen M. Carley. "COVID-19 Misinformation and Network Dynamics." Social Network Analysis and Mining, vol. 12, no. 1, 2022, pp. 1–15.
Singh, Rajeev, Yang Zhang, and Tanvi Banerjee. "Integrating GNNs and Transformer Models for Climate Discourse Analysis on Instagram." Journal of Computational Social Science, vol. 7, no. 2, 2024, pp. 115–137.
Socher, Richard, et al. "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank." Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013, pp. 1631–1642.
Zhao, Yaliang, Paul Resnick, and Qiaozhu Mei. "Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts." Proceedings of the 30th International Conference on World Wide Web (WWW), 2021, pp. 1395–1406.
Kwak, Haewoon, et al. "What Is Twitter, a Social Network or a News Media?" Proceedings of the 19th International Conference on World Wide Web (WWW), 2010, pp. 591–600.
Barabási, Albert-László. Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Plume, 2002.
Vaswani, Ashish, et al. "Attention Is All You Need." Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
Liu, Yinhan, et al. "RoBERTa: A Robustly Optimized BERT Pretraining Approach." arXiv preprint, 2019. arXiv:1907.11692.
He, Pengcheng, et al. "DeBERTa: Decoding-enhanced BERT with Disentangled Attention." Proceedings of the International Conference on Learning Representations (ICLR), 2021.
Raganato, Alessandro, and Jörg Tiedemann. "An Analysis of Encoder Representations in Transformer-Based Machine Translation." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018, pp. 2993–2998.
Tumasjan, Andranik, et al. "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment." Proceedings of the Fourth International AAAI Conference on Weblogs and SocialMedia, 2010, pp. 178–185.
Conover, Michael D., et al. "Political Polarization on Twitter." Proceedings of the Fifth International AAAI Conference on Weblogs and SocialMedia, 2011, pp. 89–96
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Copyright (c) 2025 Aisha Rahman (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
