Sociolinguistic theories have highlighted how narratives are often retold, co-constructed and reconceptualized in collaborative settings. This working paper focuses on the re-interpretation of characters, an integral part of the narrative story-world, and attempts to study how this may be computationally compared between online communities. Using online fandom - a highly communal phenomenon that has been largely studied qualitatively - as data, computational methods were applied to explore shifts in character representations between two communities and the original text. Specifically, text from the Harry Potter novels, r/HarryPotter subreddit, and fanfiction on Archive of Our Own were analyzed for changes in character mentions, centrality measures from co-occurrence networks, and semantic associations. While fandom elevates secondary characters as found in past work, the two fan communities prioritize different subsets of characters. Word embedding tests reveal starkly different associations of the same characters between communities on the gendered concepts of femininity/masculinity, cruelty, and beauty. Furthermore, fanfiction descriptions of a male character analyzed between romance pairings scored higher for feminine-coded characteristics in male-male romance, matching past qualitative theorizing. The results high-light the potential for computational methods to assist in capturing the re-conceptualization of narrative elements across communities and in supporting qualitative research on fandom.

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.

Fanfiction, a popular form of creative writing set within established fictional universes, has gained a substantial online following. However, ensuring the well-being and safety of participants has become a critical concern in this community. The detection of triggering content, material that may cause emotional distress or trauma to readers, poses a significant challenge. In this paper, we describe our approach for the Trigger Detection shared task at PAN CLEF 2023, where we want to detect multiple triggering content in a given Fanfiction document. For this, we build a hierarchical model that uses recurrence over Transformer-based language models. In our approach, we first split long documents into smaller sized segments and use them to fine-tune a Transformer model. Then, we extract feature embeddings from the fine-tuned Transformer model, which are used as input in the training of multiple LSTM models for trigger detection in a multi-label setting. Our model achieves an F1-macro score of 0.372 and F1-micro score of 0.736 on the validation set, which are higher than the baseline results shared at PAN CLEF 2023.

In the early weeks of 2023, as worry about ChatGPT and other artificial intelligence tools was ratcheting up dramatically in the public conversation, a tweet passed through the many interlocking corners of Book Twitter. "Imagine if every Book is converted into an Animated Book and made 10x more engaging," it read. Huge opportunity here to disrupt Kindle and Audible." The tweet's author, Gaurav Munjal, cofounded Unacademy, which bills itself as "India's largest learning platform"--and within the edtech context, where digitally animated books can be effective teaching tools, his suggestion might read a certain way. But to a broader audience, the sweeping proclamation that AI will make "every" book "10x more engaging" seemed absurd, a solution in search of a problem, and one predicated on the idea that people who choose to read narrative prose (instead of, say, watching a film or playing a game) were somehow bored or not engaged with their unanimated tomes.

We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment. Labeled corpus data has been compiled from narrative works hosted on Archive of Our Own (AO3), a well-known fanfiction site. In this paper, we focus on the most frequently assigned trigger type--violence--and define a document-level binary classification task of whether or not to assign a violence trigger warning to a fanfiction, exploiting warning labels provided by AO3 authors. SVM and BERT models trained in four evaluation setups on the corpora we compiled yield $F_1$ results ranging from 0.585 to 0.798, proving the violence trigger warning assignment to be a doable, however, non-trivial task.

The definition of sci-fi is notoriously slippery. For some the genre is defined by its authors and landmark novels – starting with Twenty Thousand Leagues Under the Sea author Jules Verne, or pushing things even further back to Mary Shelley's Frankenstein. Others argue that it's more about ideas than people. The Encyclopedia of Science Fiction, for example, calls the genre the "literature of cognitive estrangement." But what if crunching the data on thousands of books could give us a more definite answer?

Previously, I experimented with text generation using context-free grammars, one of the oldest techniques in natural language processing. I'll come back to CFGs in a future post. In this one I'm going to try my hand at classifiers. Automatic classification is the process by which a computer is trained to categorize an item into one or more defined buckets. A common type of classification is no doubt working on your behalf right this moment: spam filtering.