An Exploratory Study on Genre Labeling of Online Comic Reading Platforms in Taiwan

1 Research background

Comic books have become a popular form of pleasure reading. According to a market analysis published in 2023, the global comic book market is estimated to grow from USD 19.05 billion in 2025 to USD 37.15 billion in 2033[1]. Particularly, Asia Pacific is identified as the largest regional market. In Taiwan, comics are highly visible among young readers. A 2021 survey reported that around 70% of Taiwan's Generation Z is accustomed to reading comic books[2]. These reports show that comics are no longer a niche habit, but an increasingly important part of contemporary reading culture and media consumption. Traditionally, comics were serialized in magazines and later compiled into bound volumes. In recent decades, the reading environment has expanded access beyond print and made online reading platforms an important site for readers' discovery and consumption[3]. Online comic reading platforms do not simply distribute works; they also organize works through metadata, categories, and browsing structures that shape how readers encounter and interpret content[4, 5].

Existing research on comic description has focused primarily on metadata integration and cross-linking between established records and external resources. For example, Kiryakos and Sugimoto[6] proposed a linked data aggregation model for Japanese manga that incorporates bibliographic data from multiple provider types, including libraries, organizational databases, and fan web resources. These sources differ in authority, granularity, and descriptive emphasis. Generally speaking, metadata contributed by institutions usually include more authoritative records, whereas data from fan communities and organizational databases offer richer content-level details, such as plot summaries, character information, and volume-specific data. More recent studies have extended this line of inquiry by examining the use of online community data for metadata analytics[7] and the application of Linked Open Data (LOD) models to enrich the semantic representation of comic narrative structures[8].

Other studies have also explored integrated bibliographic models and descriptive frameworks. Oishi et al.[9] used linked open data to develop a dataset of comic books, anime, and video games. Nomura et al.[10] integrated the Functional Requirements for Bibliographic Records (FRBR), Wikipedia, and film/television content standards. They then created an interpretive data model that presents bibliographic information and the internal content of comic books. Similarly, Kodaira et al.[11] analyzed 3,421 subcategories under "Category: Comics" in Japanese DBpedia and summarized these subcategories using the FRBR model, finding that "Characters" accounted for the highest proportion, followed by "Series" and "Music." Since DBpedia's category structure directly incorporates information from the original Wikipedia data, these categories may reflect users' real information needs.

Beyond basic descriptive information, readers also rely on subject categorization, such as genre, when searching for pleasure reading materials in online platforms. Lin[12] found that existing appeal factor frameworks often fail to differentiate adequately between genres. Moreover, readers of different genres may have different expectations of a work. This issue has also been noted in studies on the segmentation of comic book readers. To provide a more comprehensive representation of comic book content, Akiyama and Yamanishi[13] combined topic models and large language models to generate attribute descriptions at a detailed level between genre labels and work summaries. Still, the issue of whether single genre labels are insufficient to represent the multidimensional nature of comics and related media remains unresolved. Addressing this limitation, Cho et al.[14] collected 1,597 terms from 36 online English-language animation databases and websites and then applied inductive and card-sorting methods to analyze and organize them. Their analysis revealed that anime genre terms encompass a wide range of informational features and extend far beyond simple single-label categories. Finally, Cho et al. identified 153 foci (e.g., thriller, drama) and further categorized them into nine major genre facets: audience, setting, mood, character, plot/narrative, subjects, association with other works, feature, and production.

Despite these developments, comics remain relatively underexamined within knowledge organization research, especially in terms of how online comic platforms operationalize genre categories for user access. This gap is particularly notable in Taiwan, where online comic reading is widespread. Accordingly, this exploratory study aims to compare genre-labeling approaches across the most popular online comic platforms in Taiwan. Specifically, we focused on the categorization practices in the user-facing navigation bars and investigated the genre labeling issues from the perspective of information architecture.

2 Method

We identified three of the most popular platforms in Taiwan based on website traffic rankings in 2025[15]: LINE Webtoon (Webtoon, https://www.webtoons.com), MOJOIN (https://mojoin.com/comics), and CCC Webcomics (CCC, https://www.creative-comic.tw). Webtoon and MOJOIN are commercial platforms. By contrast, CCC has a distinct institutional background. Originally launched as the Creative Comic Collection, CCC began as a print comic publishing project funded by the National Science Council Digital Archives Program of Academia Sinica in Taiwan. The project aimed to support local comic creators and promote authentic Taiwanese story topics through the print comic format. In 2020, it transitioned to an online platform format and was later renamed CCC Webcomics. It is now operated by the quasi-governmental Taiwan Creative Content Agency. Accordingly, the genre labels presented on these three platforms reflect not only the interests of readers and content creators but also the representation of topics considered culturally and socially distinctive to Taiwan.

We applied a card-sorting technique to compare genre labels across platforms. This technique enables researchers to examine different data structures, assess whether equivalent meanings are represented consistently across platforms, and evaluate the effectiveness of the underlying information architecture[16]. Our analysis focused on platform-level rather than work-level genre labels. Accordingly, we manually extracted genre terms from navigation bars of the three platforms, and individual tags assigned to specific comic works were excluded from the scope of this study. Additionally, the three platforms differ in their available language versions (i.e., Webtoon offers seven language versions, including Traditional Chinese, Korean, and English; MOJOIN is available only in Traditional Chinese; and CCC provides both Traditional Chinese and English versions). For this reason, genre terms collected for this study were primarily in Traditional Chinese and English. During February and March 2026, we collected 22, 18, and 12 Traditional Chinese terms from Webtoon, MOJOIN, and CCC, respectively, as well as 22 and 12 English terms from Webtoon and CCC. All collected terms were entered into a shared Google Sheet for collaborative sorting and review by the research team.

We then grouped identical terms, synonyms, and near-synonyms into the same row in both languages, consulting the online Cambridge Dictionary when clarification was needed. For combined terms (e.g., Horror/Thriller), we created separate rows and placed them near the corresponding single-genre terms (e.g., Horror). Because Webtoon's Traditional Chinese and English interfaces did not always align, we used URL path names to determine English equivalents for Chinese-only labels. After sorting and translation, we collected 35 unique genre terms across the three platforms. Given that comic-specific databases usually integrate richer metadata schemes[6], we adopted Cho et al.'s[14] Anime Genre Facets Framework, which was developed using genre terms extracted from anime databases and websites, for our analysis. Since this framework was originally developed for anime, we modified the operational definitions of two facets (i.e., Association with Other Works; and Production) to better align with comics. We also consulted Cho et al.'s list of 153 foci and followed their original facet assignments whenever a term in our dataset appeared in that list.

3 Result

As of March 2026, Webtoon's Traditional Chinese version (Webtoon_C) includes the largest number of genre labels (n = 22), followed by MOJOIN (n = 18), Webtoon's English version (Webtoon_E) (n = 17), and CCC (n = 11). In terms of the unique genres in the nine facets, Plot/Narrative exhibits the greatest diversity with 9 unique genres, followed by Setting (n=7), Subject (n=6), Production (n=5), Mood (n=4) and Audience (n=2). The rest of the facets (i.e., Character, Association with Other Works, and Feature) include only one genre. A detailed breakdown of these genres across the platforms is presented in Table 1.

4 Discussion

5 Conclusion

This exploratory study investigates genre labels on three major online comic platforms in Taiwan. Our analysis reveals variations across platforms and language versions in semantic scope, specificity, and naming practices. Some platforms distinguish concepts that others merge, while some labels are thematically clear and others remain vague. This finding highlights the complexity of labeling comics in digital environments, where genre labels must support both subject representation and users' access points. Moreover, genre labeling is shaped by both content characteristics and platform-specific assumptions, localization practices, and institutional contexts.

By applying a previously established Anime Genre Facets Framework, our preliminary findings confirm that genre labels represent multiple facets of comics. However, we also acknowledge that a framework developed for anime may not be fully applicable to comic genres, because their physical mediums are slightly different in nature. Thus, our next step is to conduct a user study to examine how readers actually use these labels when discovering comics online. We expect that future findings will extend the facet framework theoretically and inform the development of more consistent and user-friendly genre labels for online comic platforms.

References

1. [1] Grand View Research, Comic books market size, share & trends analysis report, 2023–2030. 2023. https://www.grandviewresearch.com/industry-analysis/comic-books-market-report.
2. [2] Focus Taiwan, Survey shows Taiwan's Gen Z are fans of comics, animations. 2021. https://ocacnews.net/article/283557.
3. [3] J. Maity, Comics in digital forms: An overview and growth of digital comics in the present era. in Contemporary Literary Review India, vol. 9, 2022.
4. [4] L. Berube, S. Makri, I. Cooke, E. Priego, and S. Wisdom, "Webcomics archive? Now I'm interested": Comics readers seeking information in web archives. in Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, ACM Press, New York, NY, pp. 412–416, 2023. https://doi.org/10.1145/3576840.3578325.
5. [5] A. Vogrinčič Čepič, T. Mascia, and J. A. Aerila, Reading for pleasure: A review of current research. in New Zealand Journal of Educational Studies, vol. 59, pp. 49–71, 2024.
6. [6] S. Kiryakos and S. Sugimoto, Building a bibliographic hierarchy for manga through the aggregation of institutional and hobbyist descriptions. in Journal of Documentation, vol. 75, pp. 287–313, 2019.
7. [7] M. Roth and Z. Kacsuk, Introducing metadata analytics: Harnessing the power of data from online fan communities for research on Japanese popular visual media. in Mechademia, vol. 17, pp. 13–31, 2025.
8. [8] S. Petiya, A Linked Open Data model for describing comic book sequences: Exploring semantic enrichment opportunities with graphic medicine. in Art Libraries Journal, vol. 48, pp. 74–79, 2023.
9. [9] K. Oishi, S. Toyota, K. Yoshioka, T. Mihara, M. Nagamori, and S. Sugimoto, [P05] マンガ・アニメ・ゲーム作品の横断的アーカイブのための作品間関係 LOD データセット開発 [A LOD dataset of relationships for linking manga, animation and game]. in デジタルアーカイブ学会誌, vol. 2, pp. 150–151, 2018.
10. [10] S. Nomura, A. Morozumi, M. Nagamori, and S. Sugimoto, マンガのためのメタデータモデルを目指したマンガのアーキテクチャの分析 [Analysis of an architecture of manga toward an integrated metadata model for manga]. in ディジタル図書館, vol. 36, pp. 3–14, 2009.
11. [11] Y. Kodaira, T. Mihara, M. Nagamori, and S. Sugimoto, マンガの配列基準としての Wikipedia カテゴリの分析 [Analysis of the Wikipedia category for the sequence standard of manga]. in ディジタル図書館, vol. 44, pp. 3–10, 2013.
12. [12] C. S. Lin, 吸引力要素與小說的組織與檢索 [Appeal factors and the organization and retrieval of fictional works]. in 圖資與檔案學刊 [Journal of InfoLib & Archives], no. 5, pp. 1–36, 2019. https://doi.org/10.6575/JILA.201912_(95).0001.
13. [13] A. Akiyama and R. Yamanishi, BERTopic を用いた漫画のあらすじに対する特性表現の検討 [Examination of characteristic expressions for manga synopses using BERTopic]. in Web インテリジェンスとインタラクション研究会 予稿集 第 20 回研究会 [Proceedings of the 20th SIG-WI], Web インテリジェンスとインタラクション研究会, pp. 125–128, 2024.
14. [14] 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.
15. [15] CCC追漫台, 誰在紅？2025 台灣商業漫畫平台作品與聲量趨勢 [Who's trending? 2025 Taiwan commercial manga platform titles and popularity trends]. 2023. https://www.creative-comic.tw/zh/special_topics/579.
16. [16] B. Martin and B. M. Hanington, Universal Methods of Design: 100 Ways to Research Complex Problems, Develop Innovative Ideas, and Design Effective Solutions. Rockport Publishers, Beverly, MA, 2012.
17. [17] J. H. Park, J. Lee, and Y. Lee, Do Webtoon-based TV dramas represent transmedia storytelling? Industrial factors leading to Webtoon-based TV dramas. in International Journal of Communication, vol. 13, pp. 13, 2019.
18. [18] CCC追漫台, 【新感覺推薦】大人蒼涼的夢境〈草風〉 [Adult's desolate dreamland "Grass wind": fresh recommendation]. 2018. https://www.facebook.com/CreativeComicCollection/posts/1822763914420781/.
19. [19] G. Unsershutz, マンガにおけるジャンルを見つめ直す―その位置付けと特徴を考察― [Rethinking genre in manga: Examining its positioning and characteristics]. in 立正大学心理学研究年報, vol. 6, pp. 21–33, 2015.

Notes

1. 1.

   "Jiakong" (架空, literally "suspended in mid-air") refers to a subgenre of fantasy set in a completely fictional universe or an alternative history with its own constructed world-setting, independent of real-world historical timelines.

   ↩
2. 2.

   "Guzhuang" (古裝, literally "ancient costume") emphasizes traditional historical aesthetics, period costumes, and settings. Unlike Western historical fiction, which often requires strict adherence to factual history, this genre prioritizes period atmosphere and may take place in fictional or highly stylized historical eras.

   ↩
3. 3.

   "Wuxia" (武俠) is a distinct Chinese genre concerning the adventures of martial artists in ancient China. It extends beyond simple physical combat ("martial arts") to encompass a complex socio-cultural philosophy centered on honor, chivalry, righteousness, and the fictional martial arts community known as the "Jianghu" (江湖).

   ↩