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Consider the time dimension: theorizing and formalizing sequential media selection

Xuanjun Gong, Richard Huskey

HUMAN COMMUNICATION RESEARCH(2024)

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Abstract
Existing media-selection theories predominantly consider media selection at a static moment-in-time. However, such theorizing is out-of-step with today's media landscape, which is dominated by sequential media consumption where future media selection is dependent on previously selected media. Ignoring the dependencies among sequential media selection leads to a failure to theorize and model the time-evolving nature of media selection. To bridge this gap, we review computational modeling methods and offer an integrative theoretical framework for studying sequential media selection. In doing so, we lay the theoretical and methodological foundation necessary for state-of-the-art research focused on understanding the underlying mechanisms of, and sequential dependencies among, media selection. Our sequential media-selection framework helps media researchers by theorizing and formalizing processes related to learning, exploration vs. exploitation, and foraging. The outcome is a manuscript that builds on existing theory and research to offer a roadmap for next-generation media-selection inquiry.
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Key words
media selection,computational modeling,reinforcement learning,exploration/exploitation,foraging
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