The purpose of this paper is to improve the understanding of relevant information features for users seeking anime recommendations.
The study uses content analysis of 396 recommendation request threads from the online forum at Anime News Network.
In total, 19 important anime information features were identified, including Work, Theme, Genre, Audience, Mood, while Artwork/Visual Style, Audio Style, and Language were mentioned less frequently. However, when mentioned, these codes were discussed with specificity and depth.
This study analyzed a relatively small number of 396 forum records, without demographic information. Using content analysis of online forum threads written by real users provided both informational breadth and depth. Future studies would benefit from using content analysis to investigate unfamiliar multimedia information and user groups.
The findings of this study can be implemented in anime-related databases and information systems to enhance organization, browsing/retrieval, and recommendation of anime, which can be further utilized for other audiovisual materials.
This is one of the few studies that investigate what anime users need and want. This research examines an understudied cultural medium, underserved by current research, despite an expanding community of anime users.
Cho, H., Schmalz, M.L., Keating, S.A. and Lee, J.H. (2018), "Analyzing anime users’ online forum queries for recommendation using content analysis", Journal of Documentation, Vol. 74 No. 5, pp. 918-935. https://doi.org/10.1108/JD-08-2017-0122Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited