Predicting and Analyzing Media Consumption Patterns: A Conceptual Approach Using Machine Learning and Big Data Analytics

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@article{1709364,
      author = {Chigozie Emmanuel Benson, Chinelo Harriet Okolo, Olatunji Oke},
      title = {Predicting and Analyzing Media Consumption Patterns: A Conceptual Approach Using Machine Learning and Big Data Analytics},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {3},
      pages = {287-295},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709364.pdf},
      abstract = {Media consumption patterns have evolved dramatically in the digital age, driven by technological advancements and the proliferation of platforms. This paper explores a conceptual approach to predicting and analyzing these patterns using advanced technologies such as machine learning and big data analytics. The discussion begins with a foundational overview of media consumption behaviors, highlighting the impact of user preferences, platform dynamics, and emerging trends. A theoretical framework is presented, detailing the role of predictive analytics in user profiling, recommendation systems, and behavioral modeling. The paper also examines the opportunities and challenges in implementing such technologies, emphasizing the benefits of personalization and targeted strategies while addressing critical issues like data privacy, algorithmic biases, and ethical considerations. Recommendations for researchers, media platforms, and policymakers are provided to guide responsible innovation. Finally, the paper identifies future research directions to enhance predictive accuracy and optimize media strategies. By balancing innovation with ethical practices, this work aims to contribute to a more effective and equitable media ecosystem.},
      keywords = {Media consumption patterns, Predictive analytics, Machine learning, Big data},
      month = {September},
  }