arXiv subjects

Ashley M. A. Fehr

Publications and source records attributed to Ashley M. A. Fehr.

7 recordsLinked to original sources

arXiv2026

The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories

Visibility in media is pivotal for identity development and for broadening societal views of gender and sexuality. Queer representation has increased in recent years, yet damaging stereotypes and tropes persist. Here, we focus on queer portrayal and its perception by audiences in fictional stories (television, film, and literature) by studying characters by their quantified archetypes which are operationalizations of common conceptions such as Hero, Diva, and Outcast. We use the archetypometrics and Fandom's LGBTQIA+ datasets to study samples of fictional characters along the trait differential spanning straight to queer. We find, quantify, and explain a seeming paradox. The characters with the highest queer score present positive primary archetypes and are typically Heroes rather than Fools, Angels rather than Demons, and Adventurers rather than Traditionalists. But evaluation across many stories for the straight-queer trait itself reveals a strong collective-writing bias towards Fool (away from Hero) and no meaningful loading for the other two dimensions. Our analysis offers a population-scale view of the complexities of queer portrayal, while also pointing to risks in blindly training on many-authored story corpora.

cs.CY↗

arXiv2026

Statistical laws and linguistics differ in naturalistic video and fictional conversations

Conversation is a cornerstone of social connection and is linked to well-being outcomes. Conversations vary widely in type with some portion generating complex, dynamic stories. One approach to studying how conversations unfold in time is through statistical patterns such as Heaps' law, which holds that vocabulary size scales with document length. Little work on Heaps' law has looked at conversation and considered how language features impact scaling. We measure Heaps' law for conversations recorded in two distinct mediums: 1. Strangers brought together on video chat and 2. Fictional characters in movies. We find that scaling of vocabulary size differs by parts of speech, suggesting a less efficient purpose in communication by medium.

cs.CL↗

arXiv2026

Stop using Media Bias/Fact Check in research

Media Bias/Fact Check (MBFC) purports to quantify the bias, credibility, and factuality of reporting for roughly 10,000 media sources, and the resulting data is commonly used in misinformation research. In the present study, we show that MBFC's methodology does not meet basic standards of rigor for academic research. Despite its widespread prevalence, studies using MBFC rarely examine it carefully, often describing it in ways that contradict its ``About'' page, or treating it as authoritative despite MBFC's disclaimer that it is ``not a tested scientific method... [but] a simple guide to the idea of a source's bias.'' We identified no papers that adequately describe MBFC as the opinions of a single person or critically engage with its methodology in order to justify proceeding with its use. We argue that MBFC's data is not neutral or accurate, but a computationally legible account of hegemony, a specious dataset for uncritical research that mistakes the familiarity of the concepts it quantifies with accuracy. Our study concludes with a call for academic researchers to stop using MBFC. MBFC's data quantifies the results of political processes, including campaigns to discredit the press, and presents them as simple facts about the world, thus reproducing the crisis misinformation scholarship exists to address.

cs.SI↗

arXiv2026

Buffy versus Bella: An archetypometric analysis and comparison

Fictional stories and characters embody and encode social norms, and their study is a powerful tool through which to understand culture and society. Vampire stories and folklore, in particular, have long both reflected and refracted people's preoccupation with disease, sexuality, death, and immortality. Here, we explore female main characters from two popular vampire franchises of the 21st century: Buffy Summers from the eponymous Buffy the Vampire Slayer and Bella Swan from the Twilight series. We employ the archetypometrics framework, built from 2,000 characters assesed across 464 semantic differential traits, to understand Buffy's and Bella's archetypes compared to one another and characters in their own stories, as well as within a larger societal context. While Buffy and Bella are female protagonists who share focus on love and romance, they differ broadly on their underlying traits and overall archetypes. Buffy -- presented as a prototypical high school cheerleader -- largely bucks traditional gender norms as an strong Adventurer-Hero. Bella -- stylized as ``not like the other girls'' -- largely conforms to traditional gender norms as a weak Outcast archetype. In each instance, our use of archetypometrics offers a detailed, character-based lens for assessing female protagonists in contemporary vampire narratives, with clear potential for broader application across other storytelling forms.

cs.CY↗

arXiv2026

Archetypes and gender in fiction: A data-driven mapping of gender stereotypes in stories

Fictional character representations reflect social norms and biases. For example, women are relatively underrepresented in television and film, irrespective of genre, and are frequently stereotyped in these media. Here, we draw on a data-driven operationalization of archetypes -- archetypometrics -- to explore the characterization of 2,000 canonically male and female characters. From an overall space of six pairs of base archetypes, we find that canonically female characters tend more toward Hero, Adventurer, Diva, and Sophisticate archetypes, while male characters, tend toward Fool, Traditionalist, Outcast, Brute and Outcast types. However, overarching patterns by gender nevertheless sustain traditional stereotypes: The seemingly positive heroic bias toward females is undercut by heroic female characters being more masculine than other female characters. We discuss the societal implications of skewed archetype representation by character gender.

cs.CY↗

arXiv2025

Detecting sub-populations in online health communities: A mixed-methods exploration of breastfeeding messages in BabyCenter Birth Clubs

Parental stress is a nationwide health crisis according to the U.S. Surgeon General's 2024 advisory. To allay stress, expecting parents seek advice and share experiences in a variety of venues, from in-person birth education classes and parenting groups to virtual communities, for example, BabyCenter, a moderated online forum community with over 4 million members in the United States alone. In this study, we aim to understand how parents talk about pregnancy, birth, and parenting by analyzing 5.43M posts and comments from the April 2017--January 2024 cohort of 331,843 BabyCenter "birth club" users (that is, users who participate in due date forums or "birth clubs" based on their babies' due dates). Using BERTopic to locate breastfeeding threads and LDA to summarize themes, we compare documents in breastfeeding threads to all other birth-club content. Analyzing time series of word rank, we find that posts and comments containing anxiety-related terms increased steadily from April 2017 to January 2024. We used an ensemble of topic models to identify dominant breastfeeding topics within birth clubs, and then explored trends among all user content versus those who posted in threads related to breastfeeding topics. We conducted Latent Dirichlet Allocation (LDA) topic modeling to identify the most common topics in the full population, as well as within the subset breastfeeding population. We find that the topic of sleep dominates in content generated by the breastfeeding population, as well anxiety-related and work/daycare topics that are not predominant in the full BabyCenter birth club dataset.

cs.SI↗

arXiv2025

A suite of allotaxonometric tools for the comparison of complex systems using rank-turbulence divergence

Describing and comparing complex systems requires principled, theoretically grounded tools. Built around the phenomenon of type turbulence, allotaxonographs provide map-and-list visual comparisons of pairs of heavy-tailed distributions. Allotaxonographs are designed to accommodate a wide range of instruments including rank- and probability-turbulence divergences, Jenson-Shannon divergence, and generalized entropy divergences. Here, we describe a suite of programmatic tools for rendering allotaxonographs for rank-turbulence divergence in Matlab, Javascript, and Python, all of which have different use cases.

cs.CL↗