arXiv subjects

Peter Sheridan Dodds

Publications and source records attributed to Peter Sheridan Dodds.

At least 19 recordsLinked to original sources

arXiv2026

Crisis-induced differences in attention towards Ukraine in Twitter 2008-2023

Aggression against Ukraine has drawn widespread international attention, particularly in the wake of the two Russian invasions into Ukrainian territory in 2014 and 2022. Although previous studies have examined social-media dynamics around these events, a comparative longitudinal data-driven view across languages is still missing. This article fills this gap by mapping added attention to "Ukraine" on Twitter in 28 languages from 2008 to 2023, using a deceptively simple DNA microarray-inspired cartography of log over-expression relative to each language's baseline frequency. This macro-scale visualization makes familiar events stand out while uncovering subtler patterns beyond the cognitive reach of any single-language audience. Most strikingly, two nearly non-overlapping language clusters emerge, one peaking around 2014 and the other around 2022 with distinct onset and decay profiles that mirror national readiness (or reluctance) to support Ukraine. By capturing attention at local, meso, and global scales, our approach offers a versatile tool for comparing relative bias across languages, user subgroups, platforms, or even historical print corpora. Ultimately, our cartographic approach reveals a troubling asymmetry: while publicly accessible data allows for an approximation of global attention patterns, the complete and unfiltered view remains largely hidden behind the closed, proprietary algorithms of major social media platforms, granting a far more comprehensive access to understanding global information flows.

cs.SI↗

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

BanglaShop-CRS: A User-Centric Bangla Dataset for Conversational Recommendation

Conversational recommender systems~(CRS) enable users to express preferences, constraints, and feedback through natural language interaction. However, existing CRS resources are concentrated in English and other high-resource languages, leaving Bangla and code-mixed Bangla--English settings underrepresented. To address this gap, we introduce BanglaShop-CRS, a large-scale user-centric synthetic Bangla conversational recommendation dataset grounded in real e-commerce behavior. It contains 27,178 multi-turn dialogues, 274,802 utterances, and 3.6M tokens across 10 product domains. Our generation pipeline incorporates user purchase histories, positive and negative feedback, and review texts to maintain consistency between dialogue content and user preferences. We evaluate BanglaShop-CRS under catalog-constrained and open-vocabulary recommendation protocols. Results show that dialogue context improves recommendation quality, while fine-tuning yields further gains across models. Human evaluation by five native Bangla-speaking annotators confirms the fluency, informativeness, logicality, and coherence of the dialogues, with significant agreement across all dimensions. Factual-grounding evaluation further shows stronger alignment with correct than shuffled user records, with substantial inter-annotator agreement ($κ=0.65$) and comparable human and GPT-5.1 judgments. BanglaShop-CRS provides a scalable benchmark for advancing conversational recommendation in Bangla.

physics.soc-ph↗

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

Archetypometrics of 'Friends'

Storytelling inherently revolves around characters. Using the television sitcom `Friends' as a case study, we investigate how well archetype vectors capture both individual characterization and the relational structure of a specific ensemble. Our work is based on the archetypometrics framework, which locates 2,000 fictional characters from 341 stories in a continuous space derived from 464 bipolar traits. We proceed in three stages: interpreting each character's archetypal profile against narrative evidence, projecting the ensemble onto ousiograms of the six essential dimensions, and measuring pairwise similarity with vector inner products. We show that the six characters of `Friends' occupy distinct archetypal positions that accord with their established identities, while the projections expose ensemble structure invisible in individual profiles, including the collapse of the Angel--Demon dimension, a signature of the sitcom's uniformly sympathetic cast. Based on inner products, we construct a similarity matrix that resolves three main kinds of relational structure: alignment (e.g., Phoebe--Joey), contrast (e.g., Phoebe--Ross), and orthogonality (e.g., Rachel--Ross and Monica--Chandler). The orthogonality of the romantic pairings affords a detailed view of relationships built on complementary rather than overlapping character traits. Overall, our case study suggests that for ensemble-based stories the archetypometric geometry is fully interpretable in narrative terms, from individual identities to the structure of the group's relationships.

physics.soc-ph↗

arXiv2026

Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning

Tokenization is a necessary component within the current architecture of many language mod-els, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient for reasonably human-like language performance (particularly with respect to inferential lexical competence), and that the emergence of human-meaningful linguistic units among tokens and current structural constraints motivate changes to existing, linguistically-agnostic tokenization techniques, particularly with respect to their roles as (1) vehicles for conveying salient distributional patterns from human language to the model and as (2) semantic primitives. We explore tokenizations from a BPE tokenizer; extant model vocabularies obtained from Hugging Face and tiktoken; and the information in exemplar token vectors as they move through the layers of a RoBERTa (large) model. Besides creating suboptimal semantic building blocks and obscuring the model's access to the necessary distributional patterns, we describe how tokens and pretraining can act as a backdoor for bias and other unwanted content, which current alignment practices may not remediate. Additionally, we relay evidence that the tokenization algorithm's objective function impacts the LLM's cognition, despite being arguably meaningfully insulated from the main system intelligence. Finally, we discuss implications for architectural choices, meaning construction, the primacy of language for thought, and LLM cognition. [First uploaded to arXiv in December, 2024.]

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

The triumphs and tragedies of fandom: Emotional arcs in NFL tweets

Online fandom communities influence public opinion toward movies, musicians, and sports teams. Using a corpus of game-referencing tweets, we measure variation in sentiment toward National Football League (NFL) teams driven by geography, game outcomes, and team performance for the 2011--2014 NFL seasons. We estimate a fandom radius for each team, identifying regions where engagement exceeds background levels of discussion. We find sentiment for both winning and losing teams is positive immediately prior to games, drops at kickoff, and rebounds slightly during halftime. After halftime however, the trajectories diverge: Sentiment for winning teams increases toward the end of the game, while sentiment for losing teams remains low, though both end up below their start of game levels. Finally, a comparison between sentiment and win percentage reveals a weak positive relationship, suggesting that while team success contributes to fandom happiness, other factors also influence how fans discuss the NFL on social media. Our work contributes to a growing body of computational social science research that quantifies the many aspects of modern fandom.

cs.CY↗

arXiv2026

Dynastic dynamics: Modelling powerful naming choices with stochastic prestige

The naming choices of powerful rulers encode millennia of cultural, political, and institutional influences. Consequently, the long-term onomastic dynamics within some of the most powerful dynasties cannot be explained by simple mechanisms such as frequency-dependent reinforcement or random name reuse. Here, we propose a minimal model that encapsulates these influences in a single highly stochastic variable, prestige, and assume that name selection is driven by the prestige accumulated by previous rulers bearing the same name within each dynasty. Using an extensive dataset spanning ten dynasties, we show that the model reproduces the pronounced inequalities in name frequencies, the long-term persistence of dominant names, and the abrupt rises in popularity observed in historical records. Our results suggest that the naming traditions of powerful rulers preserve institutional memory through reinforcement while remaining sensitive to rare historical events that can reshape the hierarchy of names across generations.

physics.soc-ph↗

arXiv2026

Self-reported archetypes and behavioral failures in Large Language Models

Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property of training, these systems exhibit persistent dispositions that shape how they interact, comply, resist, and err, yet the structure of LLM character remains poorly understood. We map the self-reported personality archetypes of 22 LLMs spanning closed-source frontier systems (GPT-4.0-5.2, Grok-3/4, Gemini 2.5 Pro/Flash, Claude Sonnet 4.5/4.6) and open-source models (Llama, DeepSeek, OLMo, and Qwen series). Each model self-rated across 464 bipolar semantic-differential trait pairs, and the resulting profiles were projected into a six-dimensional archetypal space derived from crowd-sourced ratings of 2,000 fictional characters using the Archetypometrics framework. Closed-source models' self-rating traits align with the empirical trait co-occurrence structure of human-rated fictional characters, suggesting coherent, human-like self-representations organized around combinations of four recurring archetypal dimensions: Hero, Angel, Traditionalist, and Geek. Their closest analogues include Data, Vision, and Janet. Open-source models show weaker, noisier, and internally contradictory self-representations, occupying a diffuse region of archetype space with weak structure. Cross-referencing self-reported profiles with developer constitutions reveals a consequential gap between claimed character and enacted behavior: hallucination undermines claimed precision, sycophancy complicates claimed kindness, and agentic failures contradict claimed obedience. These self-ratings should therefore be interpreted not as neutral measurements of model character, but as structured outputs of the same optimization processes that shape model behavior. This work provides a reproducible, character-grounded framework for evaluating what LLMs are, not just what they do.

cs.CL↗

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

Narrative Structure in Tropes: A Computational Analysis of `Friends'

Tropes are recurring narrative devices in television and film. We carry out a computational analysis of tropes in the sitcom Friends, using human-curated trope annotations from TVTropes, episode transcripts, and IMDb ratings. Because automatic trope detection remains challenging, we treat existing trope annotations as a curated analytical layer and focus on their downstream narrative and semantic functions. We first examine the relationship between episode-level trope frequency and audience reception. We find a statistically significant positive association between trope count and weighted IMDb ratings, although the modest explanatory power suggests that more than trope density alone explains audience evaluation. We then connect trope annotations to dialogue transcripts and represent trope-related dialogue using TF-IDF-based semantic features. Using PCA and k-means clustering, we group 1,954 distinct tropes into 15 semantically interpretable clusters. Chi-square analyses show that the six main characters are unevenly distributed across these clusters, with character-specific trope profiles that are broadly consistent with their established narrative identities. Finally, we project trope clusters into the ousiometric power-danger space to examine their semantic organization. The results show that "Physical and Sexual Comedy" occupies a region associated with relatively high danger, while "Revelation, Surprise, and Reaction" occupies a region associated with relatively high power. Overall, our work demonstrates a way to operationalize trope measurement and shows that identifiable trope clusters can provide holistic "distant reading" descriptions of characters and stories.

physics.soc-ph↗

arXiv2026

Gender Disparities in LLM-Based Intimate Partner Violence Detection

Intimate Partner Violence (IPV) is a major public health concern, and large language models (LLMs) are increasingly used for support and information-seeking in sensitive domains. We examine whether LLMs perceive relationship abuse differently depending on victim--perpetrator gender configuration. Using 475 Reddit posts from r/relationship\_advice, we generate counterfactual variants by swapping gendered identifiers to create four dyads: female--female (F/F), female--male (F/M), male--female (M/F), and male--male (M/M), where the first position denotes the victim. Four recent LLMs (GPT-5o, Gemini 3, Llama 4, and Grok 3) evaluate each variant using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results show substantial variation across models and dyads. Abuse and intent detection systematically decrease in mixed-gender dyads where the victim is male, with female perpetrator identity emerging as a consistent negative predictor of abuse recognition. Mixed-effects logistic regression confirms that gender roles significantly shape model outputs. Our findings suggest that LLMs reproduce gendered biases from online training data, with implications for support-related deployment. Code and resources are available at https://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.git.

cs.CY↗

arXiv2026

A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring

Wearable devices capture physiological and behavioral data with increasing fidelity, but the psychological context shaping these outcomes is difficult to recover from sensor data alone, limiting passive sensing utility for digital health. We examined whether ultra-brief naturalistic concern text could serve as a scalable complement to passive sensing. In a year-long study of 458 university students (3,610 person-waves) tracked with Oura rings, participants responded bimonthly to an open-ended prompt about what concerned them most; responses had a median length of three words. We compared dictionary-based, general pretrained, and domain-adapted NLP approaches using within-person mixed-effects models across nine sleep and physical activity outcomes. Weeks dominated by academic concern framing were associated with lower physical activity; weeks characterized by emotional exhaustion language were associated with poorer sleep quality and lower heart rate variability. General pretrained embeddings outperformed domain-adapted models for most outcomes, with domain adaptation showing relative advantage for autonomic outcomes. Zero-shot classification of concern topics produced no significant associations, while affective dimensions across all three methods were consistently associated with outcomes, indicating emotional register rather than topical content carries the signal. These findings offer design guidance: ultra-brief affective prompts enrich the psychological interpretability of passive physiological data at minimal burden.

cs.HC↗

arXiv2026

Simon's model does not produce Zipf's law: The fundamental rich-get-richer mechanism for any power-law size ranking

Many complex systems are composed of disparate, interacting types of varying sizes: Species abundances in ecosystems, firm sizes in markets, city populations in countries, word counts in language, etc. A longstanding mystery of complex systems is Zipf's law, which is the empirical observation that component size decreases as the inverse of component rank -- $S \propto r^{-1}$ -- and its generalization $S \propto r^{-α}$ for $α\ge 0$. Herbert Simon's 1955 theoretical rich-get-richer mechanism for system growth has prevailed as capturing the essential process. But Simon's analysis is in fact flawed: In the limit of zero innovation, the model leads to a winner-takes-all system with $α\rightarrow \infty$, rather than $α\rightarrow 1$. Here, for pure rich-get-richer systems, we derive the time-dependent innovation rate $ρ_t$ that correctly produces power-law size rankings across all $α\ge 0$. To produce Zipf's law, we uncover that $ρ_t$ must decay as the inverse of the log of the number of types, $1/\ln N$. We then show that our time-dependent innovation rate governs type emergence in any system obeying a power-law size-ranking, independent of the underlying mechanism. We demonstrate agreement between our model's output and word rankings in a collection of famous novels, while Simon's model fails. Going forward, our dynamic innovation rate mechanism provides the fundamental, Drosophila-like model for all rich-get-richer systems.

physics.soc-ph↗

arXiv2026

Complete asymptotic type-token relationship for growing complex systems with inverse power-law count rankings

The growth dynamics of complex systems often exhibit statistical regularities involving power-law relationships. For real finite complex systems formed by countable tokens (animals, words) as instances of distinct types (species, dictionary entries), an inverse power-law scaling $S \sim r^{-α}$ between type count $S$ and type rank $r$, widely known as Zipf's law, is widely observed to varying degrees of fidelity. A secondary, summary relationship is Heaps' law, which states that the number of types scales sublinearly with the total number of observed tokens present in a growing system. Here, we propose an idealized model of a growing system that (1) deterministically produces arbitrary inverse power-law count rankings for types, and (2) allows us to determine the exact asymptotics of the type-token relationship. Our argument improves upon and remedies earlier work. We obtain a unified asymptotic expression for all values of $α$, which corrects the special cases of $α= 1$ and $α\gg 1$. Our approach relies solely on the form of count rankings, avoids unnecessary approximations, and does not involve any stochastic mechanisms or sampling processes. We thereby demonstrate that a general type-token relationship arises solely as a consequence of Zipf's law.

physics.soc-ph↗

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↗

arXiv2026

Global brain drain and gain in high-potential student mobility

The mobility of high-potential individuals, particularly graduates from elite academic institutions, serves as a critical driver of global innovation and economic development. Despite its importance, granular data on the specific trajectories and demographic drivers of these flows remain scarce in traditional administrative sources. In this study, we leverage anonymized, aggregate-level digital trace data from the LinkedIn Advertising platform to map the international mobility of graduates from 1,504 QS-ranked universities across 102 countries. We find that global talent flows are highly concentrated, with the United States capturing 38.4\% of the mobile elite, followed by the United Kingdom (7.9\%) and Canada (6.8\%), while regional hubs like the United Arab Emirates (5.2\%) have emerged as significant talent magnets. Our analysis reveals a global Relative Gender Gap (RGG) of +3.16\%, indicating a modest male overrepresentation that varies sharply by destination, from extreme male skews in Ethiopia (+60.34\%) to female overrepresentation in Armenia ($-$30.77\%). Professional integration is highly structured; while Business Development and Operations are universal entry channels, technical specialization in Engineering and IT is concentrated in specific innovation hubs. Destination ``pull'' is primarily driven by economic capacity, institutional stability, and educational infrastructure, though female graduates demonstrate significantly higher sensitivity to the cost of living. These findings provide a high-resolution lens on the global ``brain circulation,'' highlighting the destination-specific comparative advantages that govern high-skilled relocation.

cs.CY↗