Investigating the Influence of Personal Memories on Video-Induced Emotions

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Abstract

This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers’ emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers’ memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.
Original languageEnglish
Title of host publicationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Pages53-61
Number of pages9
ISBN (Electronic)978-1-4503-6861-2
DOIs
Publication statusPublished - 2020
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 14 Jul 202017 Jul 2020

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
CountryItaly
CityGenoa
Period14/07/2017/07/20

Keywords

  • video affective content analysis
  • video-induced emotions

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