Volume 12 • Issue 2 • PP: 185-192 • 2023
Leveraging Social Media Data Fusion for Enhanced Student Evolution in Media Studies using Machine Learning
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In the realm of media studies, understanding student evolution is a crucial aspect for educators and researchers. However, traditional research methods often struggle to capture the dynamic nature of media consumption and the intricate interactions between individuals and media content. To address this challenge, this paper focuses on leveraging social media data fusion and machine learning techniques to enhance the comprehension of student evolution. By integrating data from diverse social media sources and employing the CATBoost algorithm with the Greedy Target-based Statistics (Greedy TBS) technique, we aim to predict student outcomes based on a comprehensive set of attributes. The results showcase the superior performance of CATBoost in accurately capturing the complexities of student evolution, surpassing other machine learning algorithms. The findings hold immense significance for educators, empowering them with valuable insights into students' behaviors, preferences, and performance.
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References
[1] Adikari, A., Burnett, D., Sedera, D., De Silva, D., & Alahakoon, D. (2021). Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning. International Journal of Information Management Data Insights, 1(2), 100022.
[2] Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., & Lee, K. C. (2020). Enhancing social media analysis with visual data analytics: A deep learning approach (pp. 1459-1492). SSRN.
[3] Sánchez-Rada, J. F., & Iglesias, C. A. (2019). Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion, 52, 344-356.
[4] Fan, C., Wu, F., & Mostafavi, A. (2020). A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters. IEEE Access, 8, 10478-10490.
[5] Rambe, P. (2012). Constructive disruptions for effective collaborative learning: Navigating the affordances of social media for meaningful engagement. Electronic Journal of e-Learning, 10(1), pp132-146.
[6] Camacho, D., Panizo-LLedot, A., Bello-Orgaz, G., Gonzalez-Pardo, A., & Cambria, E. (2020). The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Information Fusion, 63, 88-120.
[7] Pereira, J. (2016, November). Leveraging chatbots to improve self-guided learning through conversational quizzes. In Proceedings of the fourth international conference on technological ecosystems for enhancing multiculturality (pp. 911-918).
[8] Hosen, M., Ogbeibu, S., Giridharan, B., Cham, T. H., Lim, W. M., & Paul, J. (2021). Individual motivation and social media influence on student knowledge sharing and learning performance: Evidence from an emerging economy. Computers & Education, 172, 104262.
[9] Al-Garadi, M. A., Hussain, M. R., Khan, N., Murtaza, G., Nweke, H. F., Ali, I., ... & Gani, A. (2019). Predicting cyberbullying on social media in the big data era using machine learning algorithms: review of literature and open challenges. IEEE Access, 7, 70701-70718.
[10] Grewal, D., Motyka, S., & Levy, M. (2018). The evolution and future of retailing and retailing education. Journal of Marketing Education, 40(1), 85-93.
[11] Hu, K., Li, L., Tao, X., Velásquez, J. D., & Delaney, P. (2023). Information fusion in crime event analysis: A decade survey on data, features and models. Information Fusion, 101904.
[12] Gan, B., Menkhoff, T., & Smith, R. (2015). Enhancing students’ learning process through interactive digital media: New opportunities for collaborative learning. Computers in Human Behavior, 51, 652-663.
[13] Agüero-Torales, Marvin M., José I. Abreu Salas, and Antonio G. López-Herrera. "Deep learning and multilingual sentiment analysis on social media data: An overview." Applied Soft Computing 107 (2021): 107373.
[14] Guo, J., Zhang, W., Fan, W., & Li, W. (2018). Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. Journal of Management Information Systems, 35(4), 1121-1153.
[15] Sun, A. Y., & Scanlon, B. R. (2019). How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environmental Research Letters, 14(7), 073001.
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