=Paper=
{{Paper
|id=Vol-3834/paper63
|storemode=property
|title=Across the Pages: A Comparative Study of Reader Response to Web Novels in Chinese and English on Qidian and WebNovel
|pdfUrl=https://ceur-ws.org/Vol-3834/paper63.pdf
|volume=Vol-3834
|authors=Ze Yu,Federico Pianzola
|dblpUrl=https://dblp.org/rec/conf/chr/YuP24
}}
==Across the Pages: A Comparative Study of Reader Response to Web Novels in Chinese and English on Qidian and WebNovel==
https://ceur-ws.org/Vol-3834/paper63.pdf
Across the Pages: A Comparative Study of Reader
Response to Web Novels in Chinese and English on
Qidian and WebNovel
Ze Yu∗ , Federico Pianzola
Center for Language and Cognition, University of Groningen, The Netherlands
Abstract
The evolution of online reading platforms has transformed engagement with fiction, with platforms
like WebNovel bridging cultural boundaries through translated Chinese web novels. This study em-
ploys topic modeling to compare reader responses to the same stories published in Chinese on Qidian
and in English on WebNovel, focusing on English and Chinese language comments. We identify shared
and unique themes, revealing that while both communities emphasize characterization and story devel-
opment, cultural-specific expressions and platform dynamics shape readers’ interactions. Our findings
underscore the nuanced interplay between language, culture, and the affordances of digital platforms
in shaping global literary consumption and community engagement.
Keywords
Digital social reading, reader response, cross-cultural studies, topic modeling
1. Introduction
The evolution of online reading platforms, from early content-centered libraries like Project
Gutenberg and the Internet Archive to user-centered platforms such as Fanfiction.net, Archive
of Our Own, and Wattpad, highlights the growing importance of online reading. These plat-
forms not only provide a space for collective reading but also foster social interaction and
community building, offering writers interactive opportunities [7] and readers direct engage-
ment with authors [20]. Online reading communities are crucial for young readers and writers,
fulfilling emotional and social needs [25], and providing a space for marginalized voices [5, 3].
Many researches[10, 21] have explored the various aspects of Digital Social Reading (DSR),
and one of the many focuses is the reader response [11, 19]. However, current research on
storytelling and reader response has overlooked cross-cultural comparisons, with only a few
exceptions [15].This research gap has been identified not only in the field of DSR but also in
comparative literary studies, which primarily focus on understanding the cultural influences
behind literature by examining authors as transcultural readers rather than investigating the
perspectives of readers. Consequently, this focus on literary production leaves a gap in our
understanding of how readers from different cultural backgrounds interpret and engage with
CHR 2024: Computational Humanities Research Conference, December 4–6, 2024, Aarhus, Denmark
∗
Corresponding author.
£ z.yu@rug.nl (Z. Yu); f.pianzola@rug.nl (F. Pianzola)
ȉ 0009-0005-5648-6470 (Z. Yu); 0000-0001-6634-121X (F. Pianzola)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
322
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
literature. Questions such as whether cultural settings influence the understanding of top-
ics, characters, and plots, or if culture shapes reading in certain dimensions but not others,
have not been extensively investigated [29]. At the same time, the most frequently studied
books and readerships remain those with distinguished popularity, commercial success, social
impacts, and scholarly prestige in the Anglophone world, which are subject to historical and
social-cultural biases such as classism, sexism, racism, colonialism [2, 27, 15], and might not be
inclusive enough to understand reader response more broadly.
Hu et al. [15] conducted a comparative analysis on the book list and tags across Goodreads
and Douban –an international and Chinese platform, respectively –and noticed divergences in
readers’collective understanding of classics. Following up on that study, we are interested in
exploring what differences there might be in the readers’ response to the same stories when
they are read in different languages. To this end, we conducted a comparative study of different
aspects of reader response, using topic modelling to focus on the aspects that are mentioned in
the comments left on two reading platforms publishing the same stories in Chinese and English
translation. The research question that we address is: do readers of different languages and
cultural backgrounds have a different focus when commenting on a story, such as the plot,
the characters, or the setting? Do such reader responses have recognisable features that are
specific to one language and culture? The dataset we worked on are from two DSR platforms:
Qidian.com (original Chinese web novels) and Webnovel.com (English translation).
1.1. Digital Social Reading Platforms for Chinese Stories
Since its birth in the 1990s, Chinese webnovels have grown rapidly to become a new form
of Chinese literature. In recent years, Chinese webnovels have become increasingly popular
worldwide, emerging as a significant form of participatory transcultural storytelling. They not
only have a large number of loyal fan-readers in China, but have also become increasingly
popular among international readers by being translated into many languages and circulating
in different countries [16]. As defined by Michel Hockx, Chinese online literature is“Chinese-
language writing, either in established literary genres or in innovative literary forms, written
especially for publication in an interactive online context and meant to be read on-screen”[14].
The spread of Chinese literature thanks to digital technology provides opportunities for the
cultural influence of this literature abroad, but at the same time, his reach dilutes the national
attributes of Chinese online literature, loosening the boundaries between different cultural
spheres [16].
Some scholars [26, 16, 22] have pointed out that Chinese online literature has reached such
a wide audience due to its foundation in a rich literary tradition spanning classical, modern,
and contemporary China, and has inherited and integrated Western fantasy elements and Hol-
lywood narrative techniques while maintaining connectivity with other popular literature and
then localizing and recreating them in the Chinese context [16]. The imaginative worlds con-
structed in online fiction encompass history, military, war, romance, recent actual events, and
the future, all expressed in a traditional and secular Chinese style, while at the same time al-
lowing readers from all over the world to find familiar elements in them. This has fostered its
connectivity with other successful popular cultures, enabling it to attract readers from other
cultural backgrounds while giving them a sense of familiarity with the narrated world. An-
323
other explanation for the success of Chinese novels is that these online novels were created
for hedonistic reading, or “popcorn literature”(Shuang). The process of reading such novels
is supposed to be light, pleasurable, and exciting. For example, many of these Chinese novels
may cover elements of Taoism, the Three Thousand Worlds, etc., but they are not conveyed in a
very orthodox or obscure way, but rather in a simple and clear way, without requiring relevant
knowledge or a specific cultural background to understand them. Readers from any cultural
background can easily get into the story. Following Hollywood movies, Japanese animation,
and Korean dramas, Chinese web novels have become the fourth largest cultural phenomenon
in the present world [18, 16].
Qidian is one of the earliest online reading platforms in mainland China, founded in 2002,
with an innovative pay-to-read model and works covering urban, fantasy, romance, science
fiction, mystery, sports, games etc., extending to more than 200 genres. Nowadays Qidian is
one of the largest online reading platforms with more than 30 million registered readers and
more than one million stories.
WebNovel, a platform owned by the same corporation as Qidian, was ofÏcially launched in
2017, and is the first ofÏcial platform for the overseas dissemination of Chinese online literature.
It started by providing English translations of stories that were originally published on Qidian,
and later also expanded to encourage authors to freely create and upload their own stories
on this platform. According to the 2023 China Online Literature Overseas Trend Report [1],
WebNovel has launched translations of about 3,600 Chinese stories, with 238 translated works
that have been read by more than 10 million people, and 9 that have exceeded 100 million
readers. The gender distribution of WebNovel consists of 66% male readers and 34% female
readers. The top ten countries in terms of percentage of visits by country/region are the United
States, India, the United Kingdom, Indonesia, Thailand, Venezuela, Ghana, Vietnam, and Egypt,
with the United States dominating the percentage of visits to the website, accounting for 20.7%
by October 2020.
2. Parallel Corpus
The corpus creation process involved a manual search for translated novels available on Web-
novel.com within the NOVEL category, only targeting completed works.1 Subsequently, each
identified translated novel was mapped with its original counterpart on Qidian.com. This pro-
cess yielded a total of 120 novels for inclusion in the corpus. However, the copyright for 10 of
these novels on Qidian.com had expired and it was difÏcult to locate comments of these novels
on the Chinese platform, rendering them ineligible for inclusion. The final corpus consists of
110 stories [28]. According to WebNovel’s categorization visible on the website interface, these
110 stories consist of 103 Male Lead and 7 Female Lead. We scraped the metadata from each
platform and the respective comments and replies for each story, including the user profile of
the readers who left the comments. Table 1 has shown the metadata for comments and replies,
based on the information provided, we could also map the interactions between comments and
their replies.
1
The corpus metadata and the code for the analysis are available at https://github.com/zeyu-acad/Qidian-Webno
vel-Corpus; the full dataset can be accessed at [28]
324
Table 1
Corpus Metadata for WebNovel (EN) and Qidian (CN)
Source CommentId Comment ReplyId Reply BookId UserId User Rating Reply Like Create Quote Quote Quote
Content Content Level Score Amount Amount Time ReviewId Content UserId
WebNovel (EN) 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Qidian (CN) 3 3 3 3 3 3 3 - 3 3 3 3 3 3
The length of the novels exhibits a wide range, spanning from 288 to 3,588 chapters, with
corresponding word counts varying between 1,027,000 and 8,448,900 (measured in Chinese
characters). Notably, there is a slight difference in the genres and categories of stories on
Qidian and WebNovel.
We also collected the user profiles of readers who has left comments or replies on the stories,
as shown in Table 2.
Table 2
User Metadata for WebNovel(EN) and Qidian(EN)
Source UserId User Gender Level/ Writing Reading Num of Description Date Location Num
Name levelInfo Days Hours Read Books Joined Followers
WebNovel (EN) 3 3 3 3 3 3 3 3 3 3 -
Qidian (CN) 3 3 3 3 - - 3 3 - 3 3
Unlike Qidian where the readership is mainly native Chinese speakers or overseas Chinese-
using groups, readers on WebNovel do not necessarily come from the same region. We looked
into the location distribution of readers in the dataset, using the location available on the users
profiles. For the WebNovel dataset, we have the readers from 244 countries, and the top 10
locations with the most users are shown in Table 3.
Table 3
Location Distribution of users on WebNovel(EN)
Location Number Percentage (%)
Global 55100 42.80
United States 15891 12.30
Philippines 14425 11.20
India 9591 7.40
Indonesia 3231 2.50
Nigeria 2972 2.30
Malaysia 2277 1.70
Canada 2046 1.50
Australia 1602 1.20
United Kingdom 1584 1.20
Brazil 1478 1.10
We have also taken into account that book comments left on WebNovel are not necessarily in
English, so we looked into the language distribution of comments and replies using automatic
language detection.
The results (Table 4) show that English comments accounted for 72.7% of the total replies,
and English replies accounted for 68.2% of the total replies. All other languages only account
325
Table 4
Metadata for stories on both platforms
Source Genres Categories/Tags Total Comments Replies Primary Language Comments Language Distribution Replies Language Distribution
Qidian.com 14 27 2,791,837 855,577 Chinese Chinese: 95.7%, English: 0.1% Chinese: 97.2%, English: 0.05%
Webnovel.com 8 40 327,988 96,250 English English: 72.7%, Others: 27.3% English: 68.2%, Others: 31.8%
for no more than 2% of the comments/replies each. Given that our research is focused on
comparing Chinese and English readerships, we only consider replies and comments written
in English.
3. Methodology
To identify which aspects of the stories Chinese- and English-speaking readers focus on when
commenting online, we employed topic modeling. We considered both Latent Dirichlet Allo-
cation (LDA) and embeddings-based modeling. LDA has the advantage of being able to assign
several different topics to one document, by generating models with multinomial distribution
over topics. However, its efÏcacy in analyzing social media data has been highly criticized
[9, 24]. Noisy and sparse datasets are unsuitable for LDA [6] due to a lack of enough textual
features for statistical learning [4, 8].
BERTopic [13] employs a clustering embeddings approach and extends it by incorporating
a class-based variant of TF-IDF for creating topic representations. It has been proved that its
effectiveness to generate insights from short and unstructured text offers the most potential
[8]. However, with BERTopic each document is only assigned to a single topic. Even though
topic probabilities can be extracted, they are not equivalent to an actual topic distribution [8],
meaning that we may lose the ability to analyze the complexity of each document, especially
for longer comments where various aspects of a story might be commented on.
We conducted preliminary evaluation on the topic modeling of comments in both languages,
evaluating the performance of LDA and BERTopic with various core configurations and pre-
trained transformer models.
3.1. Data Preparation
Preprocessing is a critical step in ensuring data quality and consistency. Although BERTopic
does not require preprocessing of the input text, a study in comparison of LDA and BERTopic
[17] shows that topic diversity and coherence is higher in both cases with fully preprocessed
text. So we preprocessed our data as well.
We randomly examined a sample of comments and found that there were comments with
unusual expressions. For example, ”XpXPPXPXPXPXPPXPXPPXPZP”, where “xp”refers to
the experience points a user can gain to level up and get rewards on both platforms). There
are also some random typing (e.g. ”F f f f f f. F f g. F t t f. T t. T t t. T t r. R r. R e e w ”)
and sequences of emojis. We removed such comments with unconventional and inconsistent
spelling, as well as punctuation, numbers, and stopwords. We also performed lemmatization
and stemming to enhance the coherence of the analysis.
326
Figure 1: (left: LDA Topic Coherence on English comments of WebNovel; Right: LDA Topic Coherence
on Chinese comments of Qidian)
3.2. Topic Modeling Evaluation
We first applied LDA to the comments of both platforms separately. Initially, we assessed the 𝐶𝑉
[23] coherence of the topics by using a range of topic numbers for both language dataset. The
results, illustrated in 1, suggest that 30 topics could provide a good balance between the number
of topics and their coherence on both dataset. Then we conduct an evaluation including a close
reading of the topic words, to better understand the underlying meanings of the topics [8]. The
LDA generated topics were quite difÏcult to interpret, so we decided to try BERTopic2 , using the
same number of Topics. BERTopic-generated topics were easier to interpret. Moreover, when
integrated with the KeyBERT-inspired approach[12], the model performed better in generating
overall coherent yet diverse topics, even though it did not achieve the highest 𝐶𝑉 score (0.49).
Based on this evaluation, we chose to use BERTopic.
4. Results
The results of the topic modeling (Table 5) suggest that readers who leave comments in both
languages pay attention to characterisation, story development, reading experience, but they
also leave comments without any specific meaning, just with the intention to gain account
experience (WebNovel Topic 5; Qidian Topic 0). Additionally, some readers of both platforms
were able to recognise elements of the novels’ setting borrowed from other popular cultures
(WebNovel Topic 16 and 17; Qidian Topic 2).
However, beside these commonalities, the topics also reflect some features that are exclusive
to comments in each language. For example, Chinese-speaking readers on Qidian use many
formulaic sentences (Qidian Topic 11, 17, 18, 20, 22, 26), which are unique phrases or sentences
that can only be understood in the same cultural context. The literal meaning of these expres-
sions may seem irrelevant to the story text, but their derived meaning can be captured by other
readers and invite a discussion. For example, “缝缝补补又三年。”means “After mending,
the worn-out cloth can last for three more years.”and it is an expression used to refer to a
2
To address the volume of Chinese comments and mitigate memory issues during topic modeling, we utilized the
(min_df) parameter to indicate the minimum frequency of words.
327
recurring thing. In the comment, it refers to a similar event/ pattern that keeps repeating in
the story. Chinese readers tend to leave comments (Qidian Topic 7) when implicit sexual de-
pictions appear in the text, as a playful way of proving that they had noticed. This may be due
to content censorship, as the platform is aimed at an all-ages audience and the terms of service
forbid explicit sexual depictions to appear in the text. For Example,“我怀疑你在开车但我没
有证据。 ”means “I suspect you are implying a sexual scene but I don’t have any evidence.”
As for the reader response on WebNovel, we can identify some unique topics, for example,
the push for updates of the story (WebNovel Topic 3), and comments left as bookmarks, like
“Plan to read”(WebNovel Topic 26). We also observed readers express gratitude towards the
translator when they approved the good translation quality of the work (WebNovel Topic 24).
There are also comments related to sensitive themes (WebNovel Topic 27) that did not show up
in the Qidian topics. These comments (Appendix A) suggest that the stories in question may
include themes or narratives that are racially insensitive or sexist. Readers might find these
themes offensive and respond strongly to them. Similarly, when the content of a story does not
meet the readers’ expectations, readers who are deeply invested in it may have strong negative
reactions.
When retrieving topics and corresponding comments, we performed simultaneous keyword
searches on the dataset. By manually querying topic-related terms, we identified related com-
ments and found that the actual number of comments belonging to a given topic exceeded
those clustered by topic modeling. For instance, WebNovel topic 27 clustered 15 comments
annotated as Sensitive/Violence. However, a keyword search for ”racist,” which frequently ap-
peared in these comments, yielded 564 entries. Additionally, we observed that topic modeling
for both languages indicated -1 (unassigned) as the dominant cluster. This might suggest that
many comments may belong to other topics within the clustering. The presence of such a large
number of unassigned clusters warrants further investigation.
5. Conclusion
This study highlights the differences and commonalities in reader responses to Chinese web
novels across two platforms: Qidian and WebNovel. The findings indicate that readers of both
languages focus on characterization, story development, and the overall reading experience.
Additionally, interactions extend beyond the narrative, as readers leave comments to gain ac-
count experience, a behavior prompted by the platforms’affordances rather than by a di-
rect engagement with the story. Distinctive patterns also emerged in the comments of both
languages. Chinese-speaking readers on Qidian frequently use culture-specific formulaic sen-
tences and comment on implicit sexual scenes. These kinds of comments not directly related to
the story create a shared understanding among readers, fostering a collective awareness of the
subtleties in the narrative. It builds a sense of community where readers acknowledge the same
hidden layers of meaning, and might enhance the collective reading experience. Readers who
leave comments in English on WebNovel emphasize pushing for story updates, recommending
the story (or not), using comments as bookmarks, and expressing gratitude towards transla-
tors in case of high-quality translations. Notably, WebNovel also sees a higher occurrence of
sensitive themes, with comments often criticizing perceived racism and sexism in the novels.
328
Table 5
Comparison of Topics/Keywords between WebNovel and Qidian
No. WebNovel Topics/Keywords Size Annotation Qidian Topics/Keywords Size Annotation
-1 read_reader_novel_write 137,404 Outliers Outliers 1,286,482 主角 _ 没有 _ 知道
0 novel_good_book_story 92,267 Book, Chapter, Author Comments for experience 1,427,477 哈哈哈 _ 一楼 _ 知道
1 handout_review_commentary_topic 1,596 Comments for experience Location 6,251 中国 _ 日本 _ 中文 _ 上海
2 owner_branding_customer_business 1,182 Character Video Game 4,520 辐射 _ 石油 _ 化学 _ 氧气
3 release_mass_update_updatecrazy 1,152 Push for updates Story content related/Character 2,760 npc_ 地图 _ 玩家 _rbq
4 point_yyyyyyyyyyyyy_then_time 973 Recommendation (pos/neg)/ Comment for experience Specific story content related 1,420 404_ 地震 _ 停车场 _ 龙卷风
5 experience_experie_exo_expp 570 Comments for experience/Personal reading experience Specific story content related 1,191 cy_ 细胞 _ 懒癌 _ 癌细胞
6 sect_sectact_dragoon_exp 533 Story content related Story content related/Character 998 gay_ 前列腺 _gaygay_ 真的
7 daily_average_een_hga 372 Comments for experience/ Reading behavior Implicit sexual scene 941 证据 _ 开车 _ 怀疑 _ 没有
8 village_town_peasant_hillside 344 Specific story content related Specific story content related 799 空调 _ 冰箱 _ 变暖 _ 全球
9 entertainment_periodical_magazine_weekly 275 Recommendation (pos/neg) Specific story content related 523 纳米 _ 硅胶 _ 望远镜 _ 硅基
10 marry_marriage_wedding_bride 258 Specific story content related Violence 485 暴力 _serious_why_so
11 coin_img_currency_gold 174 Recommendation (pos/neg) Formula 367 名单 _ 枪毙 _ 以下 _ 目标
12 garlic_bacon_cheese_pancetta 135 Specific story content related Specific story content related 297 scp_ 基金会 _ 介入 _ 调查
13 health_science_medicine_lose 72 Covid/ Character Specific story content related 258 wifi_5g_ip_ 无线
14 chicken_farm_hate_feed 71 Specific story content related/Sensitive Theme Reading behavior 244 下载 _ 浏览器 _ 打开 _ 订阅
15 reserve_relay_share_responsibility 71 Specific story content related Specific story content related 231 退休 _ 老年痴呆 _ 痴呆 _ 心好
16 manga_anime_japanese_war 70 Japanese Manga Specific story content related 220 python_php_ 语言 _ 最好
17 flash_video_late_remote 68 Video Game/Specific story content related Formula 134 衬衫 _ 价格 _ 便士 _ 十五
18 death_dead_life_journey 64 Story content related/Character Formula 133 fbi_open_door_the
19 below_left_middle_look 58 Recommendation (pos/neg) Specific story content related 131 真空 _ 压缩 _ 密度 _flash
20 odor_sound_door_hearse 32 Story content related/Recommendation (pos/neg) Formula 119 are_you_how_old
21 fabric_complex_mobile_cloth 31 Story content related Specific story content related 105 next_boy_door_like
22 contrast_color_eye_effect 31 Story content related/Character Formula 68 惊人 _ 相似 _ 历史 _ 总是
23 mind_philosophy_hand_martial 29 Story content related/Character Specific story content related 62 java_ 天下第一 _ 最好 _ 语言
24 honey_chocolate_cereal_milk 22 Gratitude for translator Specific story content related 40 阿司匹林 _asmr_asuna_ 阿斯匹林
25 abstraction_object_define_whole 16 Comments for experience Specific story content related 35 世界 _ 版本 _ 打卡 _v1
26 plan_read_model_start 15 Bookmark Formula 26 三年 _ 三天 _ 缝缝补补 _ 我妈
27 trumpet_punish_murderer_sauce 15 Sensitive Theme/Violence Specific story content related 21 type_new_newtype_ 阿姆罗
28 ranker_company_firm_rank 12 Advertisement Specific story content related 13 pm2_ 超标 _pm10_ 行业协会
Overall, these insights into readers’ responses underscore the importance of cultural context
and platform-specific dynamics in shaping reader interactions.
6. Limitations and future work
Online platforms offer valuable resources for comparative analysis of how individuals engage
with fiction. This corpus provides an important opportunity to observe how readers from di-
verse cultural backgrounds perceive and interact with fiction in a highly interactive manner.
While this study provides valuable insights into reader response in different language settings,
several limitations need to be acknowledged. First, we only performed topic modeling on com-
ments, as comments are more directly related to the story and less likely to deviate from the
main topic compared to replies to comments, which are the next level of discussion. How-
ever, excluding replies means we may have overlooked a richer spectrum of reader response
behaviors. Integrating replies into future analyses could provide a more comprehensive under-
standing of the dynamics and nuances in reader interactions. Second, when performing topic
modeling, we set the same number of topics for both language datasets. This uniform approach
does not account for the possibility that the inherent number of topics might differ between
the two datasets. As a result, setting a fixed number of topics might lead to the aggregation of
distinct topics, thereby limiting the completeness of our exploration of reader responses. For
329
example, great amount of comments was classified to the ”Unassigned” the topic. Additionally,
the scope of our corpus was largely limited to Male Lead stories –this being the category with
more translated stories –which may not fully represent the diversity of online reading across
different cultural and social contexts. Lastly, while our analysis aimed to compare reader re-
sponse across different language settings, it did not explicitly account for cultural differences
that might influence these responses.
Future studies will focus on refining topic modeling results for a deeper analysis of topics.
We plan to incorporate replies into the model to examine the overall topics in reader responses
and conduct close reading to the topic samples including the comments that are in unsigned top-
ics. Additionally, we will add more metrics to the modeling, such as focusing on differences for
specific stories when analyzing the topics of comments and replies on both platforms. Business
reports [1] on Chinese online stories indicate that more Chinese stories are being translated
into English. Consequently, we will continue to expand our parallel corpus to include more
platforms and cover a broader range of genres and categories, thereby enhancing the general-
izability of our findings. In our effort to compare across cultures rather than just languages,
we plan to incorporate cultural context into the analysis. This will involve using a native lan-
guage identifier for English comments and replies combining with user profile to investigate
whether and how cultural backgrounds of non-native English speakers shape reader responses.
At the same time, we will take into account the possible impact on the readers’ reception of
differences between the translated edition and the original novel that may be created in order
to improve cultural adaptation.
References
[1] 2023 China Online Literature Overseas Trend Report. https://www.cssn.cn/wx/wx_ttxw
/202402/t20240226_5734785.shtml. 2023.
[2] M. Antoniak and M. Walsh. The Crowdsourced ‘Classics’ and the Revealing Limits of
Goodreads Data. 2020.
[3] C. Aragon and K. Davis. Learning in large-scale environments: Writers in the Secret Garden,
fanfiction, youth, and new forms of mentoring. With a forew. by C. Fiesler. The MIT Press,
2019.
[4] G. Cai, F. Sun, and Y. Sha. Interactive Visualization for Topic Model Curation. 2018.
[5] J. Campbell, C. Aragon, K. Davis, S. Evans, A. Evans, and D. Randall. “Thousands of
positive reviews: Distributed mentoring in online fan communities”. In: Proceedings of
the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing.
Acm, 2016, pp. 691–704.
[6] Y. Chen, H. Zhang, R. Liu, Z. Ye, and J. Lin. “Experimental explorations on short text
topic mining between LDA and NMF based Schemes”. In: Knowledge-Based Systems 163
(2019), pp. 1–13. url: https://doi.org/10.1016/j.knosys.2018.08.011.
[7] D. J. A. J. Contreras, H. G. N. Gonzaga, B. M. C. Trovela, and M. A. C. G. Kagaoan. “The
“wattyfever”: Constructs of Wattpad readers on Wattpad’s role in their lives”. In: Laguna
Journal of Arts and Sciences Communication Research 2.1 (2015), pp. 308–327.
330
[8] R. Egger and J. Yu. “A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and
BERTopic to Demystify Twitter Posts”. In: Frontiers in Sociology 7 (2022), Article 886498.
url: https://doi.org/10.3389/fsoc.2022.886498.
[9] R. Egger and J. Yu. “Identifying hidden semantic structures in Instagram data: a topic
modelling comparison”. In: Tourism Review 2021 (2021), p. 244. doi: 10.1108/tr-05-2021-
0244.
[10] S. Evans, K. Davis, A. Evans, J. A. Campbell, D. P. Randall, K. Yin, and C. Aragon. “More
than peer production: Fanfiction communities as sites of distributed mentoring”. In: Pro-
ceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social
Computing. 2017, pp. 259–272.
[11] J. Frens, R. Davis, J. Lee, D. Zhang, and C. Aragon. “Reviews matter: How distributed
mentoring predicts lexical diversity on Fanfiction.net”. In: Proceedings of the 2018 Con-
nected Learning Summit. Carnegie Mellon University: ETC Press, 2018, pp. 87–97. url:
https://doi.org/10.48550/arXiv.1809.10268v.
[12] M. Grootendorst. KeyBERT: Minimal keyword extraction with BERT. Version v0.3.0. 2020.
doi: 10.5281/zenodo.4461265. url: https://doi.org/10.5281/zenodo.4461265.
[13] M. Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure.
2022.
[14] M. Hockx. Internet Literature in China: I Eat Tomatoes, Coiling Dragon. New York: Columbia
University Press, 2015. url: https://www.wuxiaworld.com/novel/coiling-dragon-previe
w.
[15] Y. Hu, T. Underwood, G. Layne-Worthey, and J. S. Downie. Cross-Cultural Classics: Pre-
liminary Findings from Goodreads Based in the U.S. and Douban Based in China. 2023. url:
https://doi.org/10.5281/ZENODO.8107838.
[16] Z. Li. “Cross-culture, translation and post-aesthetics: Chinese online literature in/as world
literature in the Internet era”. In: World Literature Studies 15 (2023), pp. 45–61. url: http
s://doi.org/10.31577/WLS.2023.15.3.5.
[17] D. Medvecki, B. Bašaragin, A. Ljajić, and N. Milošević. Multilingual transformer and
BERTopic for short text topic modeling: The case of Serbian. 2024. url: https : / / doi . org
/10.48550/ARXIV.2402.03067.
[18] Y. Ouyang and Y. He. “Wangluo wenxue yanjiu de jige xueshu redian [On issues of
Internet literature as a research topic]”. In: Theoretical Studies in Literature and Art 39.3
(2019), pp. 174–183.
[19] F. Pianzola. Digital Social Reading: Sharing Fiction in the 21st Century. MIT Press, 2021.
doi: 10.1162/ba67f642.a0d97dee.
[20] F. Pianzola, M. Toccu, and M. Viviani. “Readers’ engagement through digital social read-
ing on Twitter: the TwLetteratura case study”. In: Library Hi Tech 40.5 (2022), pp. 1305–
1321. doi: 10.1108/lht-12-2020-0317.
331
[21] S. Rebora, P. Boot, F. Pianzola, B. Gasser, J. B. Herrmann, M. Kraxenberger, M. M. Kui-
jpers, G. Lauer, P. Lendvai, T. C. Messerli, and P. Sorrentino. “Digital humanities and
digital social reading”. In: Digital Scholarship in the Humanities 36.Supplement_2 (2021),
pp. ii230–ii250. doi: 10.1093/llc/fqab020. url: https://doi.org/10.1093/llc/fqab020.
[22] X. Ren. “Mapping globalised Chinese webnovels: Genre blending, cultural hybridity, and
the complexity of transcultural storytelling”. In: International Journal of Cultural Studies
27.3 (2024), pp. 368–386. url: https://doi.org/10.1177/13678779231211918.
[23] M. Röder, A. Both, and A. Hinneburg. “Exploring the space of topic coherence measures”.
In: Proceedings of the eighth ACM international conference on Web search and data mining.
2015, pp. 399–408. url: http://svn.aksw.org/papers/2015/WSDM%5C%5FTopic%5C%5
FEvaluation/public.pdf.
[24] M. J. Sánchez-Franco, M. H. G. S. González Serrano, M. A. d. S. dos Santos, and F. C.
Moreno. “Modelling the structure of the sports management research field using the
BERTopic approach”. In: Retos: Nuevas tendencias en educación fı́sica, deporte y recreación.
2023, pp. 648–663.
[25] R. Shang, Z. Xiao, J. Frens, and C. Aragon. “Giving and receiving: Reciprocal review
exchange in online fanfiction communities”. In: Companion Publication of the 2021 Con-
ference on Computer Supported Cooperative Work and Social Computing. 2021. url: https
://doi.org/10.1145/3462204.3481758.
[26] Y. Shao, Y. Ji, and Y. Xiao. “The Overseas Circulation of Chinese Online Literature in
the Perspective of Media Revolution”. In: Theory and Criticism of Literature and Art 38.2
(2018), pp. 119–129.
[27] R. J. So and G. Wezerek. “Opinion | Just How White Is the Book Industry?” In: The New
York Times (2020). url: https://www.nytimes.com/interactive/2020/12/11/opinion/cultu
re/diversity-publishing-industry.html.
[28] Z. Yu, F. Pianzola, and E. Tatar. Qidian-Webnovel Corpus 110. 2024. doi: 10.34894/gqxx3k.
url: https://doi.org/10.34894/GQXX3K.
[29] Y. Zhang and G. Lauer. “Introduction: Cross-Cultural Reading”. In: Comparative Litera-
ture Studies 54.4 (2017), pp. 693–701. url: https://www.muse.jhu.edu/article/680885.
A. Appendix
a. “It’s well written for the most part, but it still falls into the ever common trap of most
modern setting Chinese novels; it’s racist, sexist, homophobic and transphobic. The premise
is interesting at the beginning, but it just gets boring after a while, following the same plot
line with small tweaks of *gain new knowledge* —> *come across wacky situations than can
somehow be solved with it* —> *solve problem, but not without spending 10 chapters talking
about how everyone underestimates him* and rinse and repeat. I read up to nearly chapter 500,
and the quality just decreases. Even if all those things still don’t put you off, you still don’t
need to read this, I’m sure you can find some other face slapping system novel to read, god
332
knows there’s so f***ing many out there, some of which should probably be at least a bit less
racist.”
b. “If you are a big racist then you may like this novel. If not then, as 90% of Chinese novel,
you will understand that the author is a ******* racist and that he doesn’t understand what
people think of China.I mean: who doesn’t know that China is the country with the highest
number of people who ”disappeared” mysteriously and the number 1 in terms of extermination
of minorities?”
333