Big Data and Machine Learning to Study The Media Consumption Behavior of the New Generation

Abstract

This study, “The Application of Big Data and Machine Learning in the Analysis of Media Consumption Behavior among Young Generations,” aims to (1) examine media consumption patterns of Generation Z and Generation Alpha audiences in Thailand using Big Data and Machine Learning perspectives, (2) analyze the role of artificial intelligence (AI) and recommendation algorithms in shaping media selection behaviors, and (3) propose communication and marketing strategies aligned with digital-era media consumption. The research is grounded in the theoretical frameworks of Uses and Gratifications Theory, Technological Determinism, and the concepts of Filter Bubble and Echo Chamber, which together explain user motivations, technological influence, and algorithm-driven personalization in contemporary media environments. A sequential explanatory mixed-methods design was employed. The quantitative phase involved a stratified random sample of 120 respondents aged 13–26 years. Data were collected through a five-point Likert-scale questionnaire measuring five composite dimensions: entertainment/relaxation, self-expression/interaction, learning/information seeking, perceived algorithmic influence, and privacy concerns. Data analysis included descriptive statistics, k-means cluster analysis, and logistic regression. The qualitative phase consisted of semi-structured in-depth interviews with three key informants—digital marketing and AI experts, a consumer behavior psychologist, and a youth representative—analyzed using thematic analysis.

Findings indicate that media consumption among Gen Z and Gen Alpha is predominantly video-centric, with short-form video platforms playing a central role. Recommendation algorithms significantly influence platform choice and content exposure, reinforcing habitual consumption patterns. Cluster analysis identified three distinct user profiles with differing motivations and engagement styles. The results highlight the strong interplay between user gratifications and algorithmic systems, supporting both Uses and Gratifications Theory and Technological Determinism. Based on these findings, the study proposes a strategic framework emphasizing mobile-first, short-form content combined with responsible personalization to balance marketing effectiveness, informational credibility, and user privacy within the Thai digital media context.