The Integration of Deep Learning and Machine Learning for Enhanced Social Media Analytics
Keywords:
algorithmic bias, data privacy, deep learning, explainable AI, machine learning, social media analytics, unstructured dataAbstract
Social media platforms are a dominant force in generating vast volumes of unstructured data, creating unprecedented opportunities and challenges in analytics. Traditional tools struggle with the sheer scale, diversity, and velocity of social media data. The emergence of machine learning (ML) and deep learning (DL) has revolutionized the field, providing sophisticated methods for deriving insights into user sentiment, community dynamics, and behavior. This paper explores the roles of ML and DL in social media analytics (SMA), detailing their applications in sentiment analysis, social network analysis, recommendation systems, and fake news detection. It also examines critical challenges such as data privacy, algorithmic bias, and scalability. Finally, future directions, including hybrid models and explainable AI (XAI), are discussed, emphasizing the importance of interdisciplinary approaches to addressing complex societal and technical issues.
References
[1] J. Choi, J. Yoon, J. Chung, B.-Y. Coh, and J.-M. Lee, “Social media analytics and business intelligence research: A systematic review,” Information Processing & Management, vol. 57, no. 6, p. 102279, Nov. 2020.
[2] P. Koukaras and C. Tjortjis, “Social Media Analytics, Types and Methodology: Applications of Learning and Analytics in Intelligent Systems,” in Machine Learning Paradigms, vol. 1, G. A. Tsihrintzis, M. Virvou, E. Sakkopoulos, and L. C. Jain, Eds. Cham: Springer International Publishing, 2019, pp. 401–427.
[3] J. Mayol, “Social media analytics,” Surgery, vol. 174, no. 3, pp. 735–740, Sep. 2023.
[4] C. W. Holsapple, S.-H. Hsiao, and R. Pakath, “Business social media analytics: Characterization and conceptual framework,” Decision Support Systems, vol. 110, pp. 32–45, Jun. 2018.
[5] S. Stieglitz, M. Mirbabaie, B. Ross, and C. Neuberger, “Social media analytics – Challenges in topic discovery, data collection, and data preparation,” International Journal of Information Management, vol. 39, pp. 156–168, Apr. 2018.
[6] S. Neelakandan et al., “Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–13, Jun. 2022.
[7] K. Hayawi, S. Saha, M. M. Masud, S. S. Mathew, and M. Kaosar, “Social media bot detection with deep learning methods: a systematic review,” Neural Comput & Applic, Mar. 2023.
[8] Y. Chen, Y. Lv, X. Wang, L. Li, and F.-Y. Wang, “Detecting Traffic Information From Social Media Texts With Deep Learning Approaches,” IEEE Trans. Intell. Transport. Syst., vol. 20, no. 8, pp. 3049–3058, Aug. 2019.
[9] J. Kim, J. Lee, E. Park, and J. Han, “A deep learning model for detecting mental illness from user content on social media,” Sci Rep, vol. 10, no. 1, Jul. 2020.
[10] S. Agrawal and A. Awekar, “Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings,” in Advances in Information Retrieval, vol. 10772, G. Pasi, B. Piwowarski, L. Azzopardi, and A. Hanbury, Eds. Cham: Springer International Publishing, 2018, pp. 141–153.
[11] M. Mameli, M. Paolanti, R. Pietrini, G. Pazzaglia, E. Frontoni, and P. Zingaretti, “Deep Learning Approaches for Fashion Knowledge Extraction From Social Media: A Review,” IEEE Access, vol. 10, pp. 1545–1576, 2022.
[12] L.-C. Chen, C.-M. Lee, and M.-Y. Chen, “Exploration of social media for sentiment analysis using deep learning,” Soft Comput, vol. 24, no. 11, pp. 8187–8197, Jun. 2020.
[13] M. M. Agüero-Torales, J. I. Abreu Salas, and A. G. López-Herrera, “Deep learning and multilingual sentiment analysis on social media data: An overview,” Applied Soft Computing, vol. 107, p. 107373, Aug. 2021.
[14] F. Monti, F. Frasca, D. Eynard, and D. Mannion, “Fake news detection on social media using geometric deep learning.”
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