Reciprocity in Reviewing on Fanfiction.Net

Froelich, Niamh; Liu, Arthur; Shang, Ruoxi; Xiao, Zile; Neils, Travis; Frens, Jenna; Aragon, C.

doi:10.1007/978-3-030-78645-8_5

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To automatize this analysis, researchers have used the Measure of Textual Lexical Diversity (MTLD) at the level of chapters within each story and found that the MTLD increased with the number of reviews received (Frens et al 2018). The idea that writers help each other as a community received further evidence in a follow-up study (Froelich et al 2021). The authors trained the BERT classifier to recognize reviews that provided specific rather than generic feedback, and they found a moderate (albeit statistically significant) correlation between giving and receiving constructive feedback.…”

Section: Background: Natural Language Processing and Fanfictionmentioning

confidence: 99%

Big data meets storytelling: using machine learning to predict popular fanfiction

Nguyen,

Zigmond,

Glassco

et al. 2024

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Fanfictions are a popular literature genre in which writers reuse a universe, for example to transform heteronormative relationships with queer characters or to bring romance into shows focused on horror and adventure. Fanfictions have been the subject of numerous studies in text mining and network analysis, which used Natural Language Processing (NLP) techniques to compare fanfictions with the original scripts or to make various predictions. In this paper, we use NLP to predict the popularity of a story and examine which features contribute to popularity. This endeavor is important given the rising use of AI assistants and the ongoing interest in generating text with desirable characteristics. We used the main two websites to collect fan stories (Fanfiction.net and Archives Of Our Own) on Supernatural, which has been the subject of numerous scholarly works. We extracted high-level features such as the main character and sentiments from 79,288 of these stories and used the features in a binary classification supported by tree-based methods, ensemble methods (random forest), neural networks, and Support Vector Machines. Our optimized classifiers correctly identified popular stories in four out of five cases. By relating features to classification outcomes using SHAP values, we found that fans prefer longer stories with a wider vocabulary, which can inform the prompts of AI chatbots to continue generating such successful stories. However, we also observed that fans wanted stories unlike the original material (e.g., favoring romance and disliking when characters are hurt), hence AI-powered stories may be less popular if they strictly follow the original material of a show.

show abstract

Section: Background: Natural Language Processing and Fanfictionmentioning

confidence: 99%