Bridging the Gap: Harnessing the Power of Machine Learning and Big Data for Media Research

Li-jing Arthur Chang

This paper explores the use of machine learning and big data to enhance mass media research. It covers topics such as principles of machine learning relevant to media studies, integration of computational methods with media research, data collection and preprocessing techniques, visualization of research findings, machine learning research tools, data quality and bias, ethical considerations, cross-disciplinary skills and knowledge, and best practices in data-driven research. Additionally, the paper addresses the status of media research with machine learning and big data, discussing its impact and contributions to academia and society, as well as the future challenges it may face.

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