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Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position

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  • Kate Burleson-Lesser
  • Flaviano Morone
  • Paul DeGuzman
  • Lucas C Parra
  • Hernán A Makse

Abstract

Videos and commercials produced for large audiences can elicit mixed opinions. We wondered whether this diversity is also reflected in the way individuals watch the videos. To answer this question, we presented 65 commercials with high production value to 25 individuals while recording their eye movements, and asked them to provide preference ratings for each video. We find that gaze positions for the most popular videos are highly correlated. To explain the correlations of eye movements, we model them as “interactions” between individuals. A thermodynamic analysis of these interactions shows that they approach a “critical” point such that any stronger interaction would put all viewers into lock-step and any weaker interaction would fully randomise patterns. At this critical point, groups with similar collective behaviour in viewing patterns emerge while maintaining diversity between groups. Our results suggest that popularity of videos is already evident in the way we look at them, and that we maintain diversity in viewing behaviour even as distinct patterns of groups emerge. Our results can be used to predict popularity of videos and commercials at the population level from the collective behaviour of the eye movements of a few viewers.

Suggested Citation

  • Kate Burleson-Lesser & Flaviano Morone & Paul DeGuzman & Lucas C Parra & Hernán A Makse, 2017. "Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0168995
    DOI: 10.1371/journal.pone.0168995
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    References listed on IDEAS

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    1. Lester C Loschky & Adam M Larson & Joseph P Magliano & Tim J Smith, 2015. "What Would Jaws Do? The Tyranny of Film and the Relationship between Gaze and Higher-Level Narrative Film Comprehension," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-23, November.
    2. Jacek P. Dmochowski & Matthew A. Bezdek & Brian P. Abelson & John S. Johnson & Eric H. Schumacher & Lucas C. Parra, 2014. "Audience preferences are predicted by temporal reliability of neural processing," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
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    Cited by:

    1. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.

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