Beyond Canonical Texts: A Computational Analysis of Fanfiction

Milli, Smitha; Bamman, David

doi:10.18653/v1/d16-1218

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…Data from fanfiction has been used in NLP research for a variety of tasks, including authorship attribution (Kestemont et al, 2018), action prediction (Vilares and Gómez-Rodríguez, 2019), finegrained entity typing (Chu et al, 2020), and tracing the sources of derivative texts (Shen et al, 2018). Computational work focusing on characterization in fanfiction includes the work of Milli and Bamman (2016), who found that fanfiction writers are more likely to emphasize female and secondary characters. Using data from WattPad, a platform that includes fanfiction along with original fiction, Fast et al (2016) find that portrayals of gendered characters generally align with mainstream stereotypes.…”

Section: Fanfiction and Nlpmentioning

confidence: 99%

FanfictionNLP: A Text Processing Pipeline for Fanfiction

Yoder¹,

Khosla²,

Shen³

et al. 2021

Proceedings of the Third Workshop on Narrative Understanding

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Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution.

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Section: Fanfiction and Nlpmentioning

confidence: 99%

FanfictionNLP: A Text Processing Pipeline for Fanfiction

Yoder¹,

Khosla²,

Shen³

et al. 2021

Proceedings of the Third Workshop on Narrative Understanding

View full text Add to dashboard Cite

show abstract

“…Data from fanfiction has been used in NLP research for a variety of tasks, including authorship attribution (Kestemont et al, 2018), action prediction (Vilares and Gómez-Rodríguez, 2019), finegrained entity typing , and tracing the sources of derivative texts (Shen et al, 2018). Computational work focusing on characterization in fanfiction includes the work of Milli and Bamman (2016), who found that fanfiction writers are more likely to emphasize female and secondary characters. Using data from WattPad, a platform that includes fanfiction along with original fiction, Fast et al (2016) find that portrayals of gendered characters generally align with mainstream stereotypes.…”

Section: Fanfiction and Nlpmentioning

confidence: 99%

Proceedings of the Third Workshop on Narrative Understanding

2021

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While natural language understanding of longform documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text -scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised 'interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.

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“…For HPAC, we used fan fiction (and only fan fiction texts) from https://www. fanfiction.net/book/Harry-Potter/ and a version of the crawler by Milli and Bamman (2016). 3 We collected Harry Potter stories written in English and marked with the status 'completed'.…”

Section: Data Crawlingmentioning

confidence: 99%

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Vilares

Gómez-Rodríguez

2019

Proceedings of the 2019 Conference of the North

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We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the world of the Harry Potter fantasy novels and inferring what spell will be cast next given a fragment of a story. Spells act as keywords that abstract actions (e.g. 'Alohomora' to open a door) and denote a response to the environment. This idea is used to automatically build HPAC, a corpus containing 82 836 samples and 85 actions. We then evaluate different baselines. Among the tested models, an LSTM-based approach obtains the best performance for frequent actions and large scene descriptions, but approaches such as logistic regression behave well on infrequent actions.

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Beyond Canonical Texts: A Computational Analysis of Fanfiction

Cited by 16 publications

References 16 publications

FanfictionNLP: A Text Processing Pipeline for Fanfiction

FanfictionNLP: A Text Processing Pipeline for Fanfiction

Proceedings of the Third Workshop on Narrative Understanding

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