From Genre Categorization to User Discovery: Knowledge Organization of Online Comic Platforms in Taiwan

Abstract

Online comic readers rely on a wide range of descriptions to find new works, yet these user-centric terms are often different from the categorization terms used by platforms. Moreover, due to the subjective nature of “aboutness” judgments, the same comic work is often categorized under different themes on different platforms, creating a gap between the platform’s categorization and users’ natural language descriptions. This work-in-progress master’s thesis proposal aims to explore the current status of genre categorization on online comic platforms in Taiwan and compare them with readers’ perspectives. After collecting terms from popular Taiwanese platforms and performing content analysis, interviews will be conducted to gain a deeper understanding of user feedback. Preliminary analysis shows that platform category terms are multifaceted, encompassing mood, setting, and audience. Additionally, semantic inconsistencies across platforms, and between different language versions of the same platform, are also observed. Furthermore, a platform’s background (e.g., quasi-governmental vs. commercial) leads to significant differences in organizing logic, increasing the cognitive load for readers. Through this DCMI Student Forum, the author expects to discuss and receive feedback on how to integrate readers’ perspectives into existing comic genre categorization.

1 Research backgrounds

Comics are a popular form of pleasure reading. Comic readers seek out works that interest them in ways similar to those of novel readers. Besides the content and theme of a work itself, readers also consider the emotional experience or atmosphere it evokes when searching for new works. As comic publishing has gradually shifted from traditional print to online serialization, the logic of categorization has also changed. Traditionally, comic works are categorized by content type, theme, or target audience. In the online environment, however, additional categorization facets may need to be considered, such as conventional elements or production characteristics.

Readers can effectively discover their interested works if online comic platforms assign comic works to proper categories. In order to decide into which category the comic work should be placed, it is necessary to identify the comic’s subject. This process relies on determining a work’s “aboutness”, by considering the work’s thematic content and structural genre or form[1]. It also heavily depends on the analyst’s professional expertise to accurately interpret the resource. Once the nature of an object is defined, it can be organized through classification, the process of grouping entities based on perceived similarities to create meaningful conceptual categories[2]. However, due to its subjective nature, there is often no single correct method for determining aboutness; hence, it is possible for the same comic to be assigned to variant subjects on different online platforms, which may cause confusion for users in the searching process.

Besides the category differences between online platforms, research on users’ online forum queries reveals there may exist a gap between platforms’ categories and users’ natural language descriptions. Studies of online forums (e.g., Anime News Network and Naver Knowledge-iN) using qualitative analysis reveal that when users rely on characteristics like title, genre, and theme, they frequently incorporate highly specific contextual needs, including mood/tone, artistic style, plot elements, and “negative preferences” (e.g., “Don’t Like” criteria)[3, 4]. These studies emphasize that genre, theme, and trope are culturally situated and often indistinguishable, requiring a higher level of granularity than hierarchical systems currently provide. Research also shows that audiences of both anime and comics use similar descriptive terms when making recommendation requests.

To analyze the attributes/genres used when categorizing anime, Cho et al.[5] developed a faceted classification framework by analyzing nearly 1,600 terms from 36 databases and websites. This framework identifies nine key facets: Audience (target group), Setting (time/place), Mood (emotional state), Character (appearance/persona), Plot/Narrative (development), Subjects (main themes), Association with other work (intertextuality/media links), Feature (specific conventions/tropes) and Production (technique/origin). Within this framework, ambiguous genres and tropes are categorized under Subjects and Features. Subjects mean the “aboutness” of a work, usually describing themes or central ideas of the work, while the Feature facet contains recurring elements. These terms are known as “tropes” that appear in works and serve as granular discovery points for the audience.

While these facets offer a comprehensive lens for animation, the direct extensibility of this framework to the comic book domain requires further investigation. Due to differences in media-specific characteristics between comics and animation, an integrated study is necessary to refine these classification terms for online comic platforms. To date, however, there remains a lack of research that evaluates the effectiveness of genre categorization within the context of online comic platforms. Thus, the purpose of this study is to explore the current categorizing practices of online comic genres and reader behaviors. The following research questions will be addressed in this study:

1. What are the current practices of online comic platforms’ genre categorization?

   1. What are the differences among different platforms?
2. What are readers’ perspectives of online comic platforms’ genre categorization practices?
3. How do online comic platforms’ genre categorization practice impact readers’ information behaviors?

2 Methodology

This submission is a work-in-progress master’s thesis proposal. It extracts genre terms from existing online comic book platforms then organizes the collected categories and keywords using card sorting. Three platforms were selected based on statistical data from annual industry market reports in Taiwan; the data collection process followed the practical approach of Cho[5], according to its strong alignment with the functional goals of digital content platforms. Afterward, the author will interview readers about their experiences using online comic book platforms to understand their perspectives and evaluations of genre categories.

3 Preliminary findings/expected contributions

The preliminary analysis results of three online comic platforms have been submitted to DCMI 2026 as a full paper. This submission focuses on proposing the research design that aims to extend those preliminary findings into a user-centered investigation.

The preliminary results of three online comic platforms compare the genre categories of three popular online comic platforms in Taiwan (i.e., CCC, MOJOIN, and LINE Webtoon). The results reveal that platform “genres” are actually multifaceted constructs encompassing Mood, Setting, and Audience, echoing previous research on users’ natural language descriptions. However, semantic inconsistencies exist across these platforms. Some platforms employ granular, topic-based tags to describe specific narrative elements, while others rely on broad, high-level genre labels. Furthermore, semantic divergence was found in LINE Webtoon’s versions (i.e., Mandarin and English) of the same genre. On the other hand, the background of platforms (e.g., quasi-governmental vs. commercial) leads to distinct categorization of the same work, which may cause extra cognitive load for users who use various platforms at the same time. Moreover, in some cases, a single work is even assigned to multiple categories, which may also confuse readers. These findings highlight a tension between platforms’ genre categorizations and practical resource discovery, which may confuse readers and lead to ambiguity in interpretations. This finding establishes the clear necessity for the next phase of this study: investigating how readers navigate these discrepancies to find comics.

4 Expected outcome in student forum

As this study is moving from the platform-based genre term analysis to the user-study phase, participating in the DCMI 2026 Student Forum offers a valuable opportunity to refine the methodology through discussion. This research focuses on the differences between existing genre categorization frameworks, platform labeling practices, and user interpretations within online comics. By engaging with the metadata community, the author hopes to seek advice on how to analyze these differences and organize these genre terms into a reference list that meets both existing platform genre practices and the readers’ expectations. As this study is in its formative stage, specific parameters for the user interview phase are currently being finalized. Thus, one of the primary objectives of participating in the Student Forum is to seek feedback from the community on refining this upcoming user study design. Instead of automated classification, the author is particularly interested in how a reader-oriented approach serves as a foundation for more meaningful metadata standards and digital resource management systems. Feedback from the Student Forum will be very helpful in refining this study.

Acknowledgements

The author would like to express her sincere gratitude to her supervisor, Dr. Li-Min Huang, for providing research guidance, direction, and constructive feedback on the writing of this work-in-progress proposal. The author also appreciates the feedback on writing and presentation provided by her cohort.

References

1. [1] R. M. Holley and D. N. Joudrey, Aboutness and conceptual analysis: A review. in Cataloging & Classification Quarterly, vol. 59, pp. 159-186, 2020.
2. [2] B. Hjørland, Classification. in Knowledge Organization, vol. 44, pp. 97-128, 2017.
3. [3] H. Cho, M. L. Schmalz, S. A. Keating, and J. H. Lee, Analyzing anime users’ online forum queries for recommendation using content analysis. in Journal of Documentation, vol. 74, pp. 918-935, 2018.
4. [4] J. H. Lee, Y. Shim, and J. Jett, Analyzing user requests for anime recommendations. in Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM Press, New York, NY, pp. 269-270, 2015.
5. [5] H. Cho, T. Disher, W. C. Lee, S. A. Keating, and J. H. Lee, Facet analysis of anime genres: The challenges of defining genre information for popular cultural objects. in Knowledge Organization, vol. 47, pp. 13-30, 2020. https://doi.org/10.5771/0943-7444-2020-1-13.