Collective user switching behavior reveals the influence of TV channels and their hidden community structure

Mingyan Wang, An Zeng and Xiaohua Cui

Physica A: Statistical Mechanics and its Applications, 2022, vol. 606, issue C

Abstract: Television is the primary medium through which most families access entertainment and information in their daily lives. Thus, understanding users’ TV viewing behavior is meaningful for several practical issues, such as evaluating the influence of TV channels and providing personalized TV recommendations. However, most existing works regarding TV viewing data are limited to basic statistics (e.g., TV ratings). In this paper, we analyze a large-scale TV viewing dataset for a city in China via a complex network approach. We construct a directed network that characterizes the collective channel-switching behavior of viewers. By using the PageRank method, we reveal the influential TV channels that are more in line with people’s expectations than their rankings based on simple TV ratings. We further construct a network in which channels are linked according to their similarity in users’ switching preferences. This network exhibits a clear community structure, which can help TV stations understand which channels are in the bottleneck and which channels have potential. Overall, our work provides a system perspective to evaluate TV channels and their relationships.

Keywords: TV ratings; Complex network; PageRank; Community detection (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:606:y:2022:i:c:s0378437122006860

DOI: 10.1016/j.physa.2022.128105

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