Media Diversity Forecasting: A Longitudinal Study Using Hybrid Machine Learning Model for Predictive Insights into Community Representation (2004-2024)
This research addresses the growing interest in diverse media representation by investigating long-term trends across six communities: African, Asian, European, Hispanic, Indigenous, and Middle Eastern. Spanning news, social media, and entertainment, our study introduces a novel forecasting system using a hybrid of Long-Term Memory (LSTM) neural networks, Autoregressive Integrated Moving Average (ARIMA), and Prophet models. Two decades of data (2004-2024) were scraped from diverse open sources and media archives, and a unique engagement metric was proposed. The models demonstrated high accuracy, significantly improving upon benchmark studies in social media forecasting. This project also features a user-friendly web application, enabling stakeholders to gain predictive insights. This work offers actionable, data-driven insights to evaluate and improve media inclusivity, setting groundwork for future cultural analytics, policy development and ethical media production.
References
[1]
Abbasi, A., Javed, A. R., Iqbal, F., Kryvinska, N., & Jalil, Z. (2022). Deep Learning for Religious and Continent-Based Toxic Content Detection and Classification. Scientific Reports, 12, Article No. 17478. https://doi.org/10.1038/s41598-022-22523-3
[2]
All Together Now (2019). Social Commentary and Racism (2019). All Together Now. https://www.alltogethernow.org.au/wp-content/uploads/2019/11/Social-Commentary-and-Racism-2019-1.pdf
[3]
Asur, S., & Huberman, B. A. (2010). Predicting the Future with Social Media. 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 492-499). IEEE. https://doi.org/10.1109/wi-iat.2010.63
[4]
Erigha, M. (2015). Race, Gender, Hollywood: Representation in Cultural Production and Digital Media’s Potential for Change. Sociology Compass, 9, 78-89. https://doi.org/10.1111/soc4.12237
[5]
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28, 2222-2232. https://doi.org/10.1109/tnnls.2016.2582924
[6]
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[7]
Hofhuis, J., Gonçalves, J., Schafraad, P., & Wu, B. (2024). Examining Strategic Diversity Communication on Social Media Using Supervised Machine Learning: Development, Validation and Future Research Directions. Public Relations Review, 50, Article 102431. https://doi.org/10.1016/j.pubrev.2024.102431
[8]
Hui, J. (2025). Media Representation of Minorities and Its Impact on Public Perception in Singapore. International Journal of Sociology, 9, 52-64. https://doi.org/10.47604/ijs.3203
[9]
Ingelbrecht, K., Singh, Y., Durgadmath, N., Moieni, R., & Lee, N. (2024). Cultural Diversity and Urban Features: An Australian Case Study. Open Journal of Social Sciences, 12, 470-486. https://doi.org/10.4236/jss.2024.127034
[10]
Khan, W., Ghazanfar, M. A., Javed, A., Khan, F. U., Shah, Y. A., & Ali, S. (2024). Stock Market Prediction Using LSTM Model on the News and Social Media Data. VFAST Transactions on Software Engineering, 12, 117-133. https://doi.org/10.21015/vtse.v12i4.1949
[11]
Loecherbach, F., Moeller, J., Trilling, D., & van Atteveldt, W. (2020). The Unified Framework of Media Diversity: A Systematic Literature Review. Digital Journalism, 8, 605-642. https://doi.org/10.1080/21670811.2020.1764374
[12]
Moieni, R., Mousaferiadis, P., & Pateel, P. (2022). An Analytical Approach to Measure the Cultural Diversity Mutuality between Two Communities. NeuroQuantology 20, 105-120.
[13]
Moieni, R., Mousaferiadis, P., & Roohi, L. (2023). A Study on Diversity Prediction with Machine Learning and Small Data. Open Journal of Social Sciences, 11, 18-31. https://doi.org/10.4236/jss.2023.112002
[14]
Rios, E., Hou, S., Lee, N., & Moieni, R. (2024). Small Scale Predictive Analysis of Gender Balance in Australia Using Grey Models: Integrating Labour Force and Migration Data. Open Journal of Social Sciences, 12, 448-469. https://doi.org/10.4236/jss.2024.127033
[15]
Rodrigo-Ginés, F., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A Systematic Review on Media Bias Detection: What Is Media Bias, How It Is Expressed, and How to Detect It. Expert Systems with Applications, 237, Article 121641. https://doi.org/10.1016/j.eswa.2023.121641
[16]
Żerebecki, B. G., Opree, S. J., & Hofhuis, J. (2025). Communication Studies, 76, 186-204. https://doi.org/10.1080/10510974.2024.2390703
