Exploring the Information Cues of Danmaku Comments to Stimulate Users' Affective Generation in Reaction Videos

Xujie Ye*, Yuxiang (Chris) Zhao, Jinhao Li, Yan Zhang, Preben Hansen

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Citations (Scopus)

Abstract

Reaction video, a new form of online video that records users' instant reactions to a particular thing, has emerged on social media in recent years. Its unique content composition and hedonic and emotional characteristics make the information cues that influence the affective generation in danmaku comments quite different from those in traditional videos. To explore the information cues of danmaku comments to stimulate users’ affective generation in reaction videos, we conduct thematic coding using the content analysis method by selecting the danmaku resources, video content, and reactors’ responses from 11 popular videos in different categories as samples to identify information cues that influence user affects in danmaku comments. The preliminary findings show that there are three main types of information cues in the reaction videos: the content of the original video, reactors’ reaction and danmaku comments, which could trigger danmaku users’ affect in reaction videos from the perspective of orientation type, parasocial interaction, and peer influence, respectively. 86th Annual Meeting of the Association for Information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.
Original languageEnglish
Pages (from-to)723-727
JournalProceedings of the Association for Information Science and Technology
Volume60
Issue number1
Online published22 Oct 2023
DOIs
Publication statusPublished - Oct 2023

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. Research Unit(s) information for this record is based on his previous affiliation.

Research Keywords

  • Affective generation
  • Content analysis
  • Danmaku
  • Information cues
  • Reaction video

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