arXiv subjects

Petter Törnberg

Publications and source records attributed to Petter Törnberg.

At least 19 recordsLinked to original sources

arXiv2026

From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations

Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emotions, and political opinions. While prior work shows that personas compress outputs into stereotypical caricatures, there is no comprehensive study of how these caricatures evolve into collective artifacts when they interact. Using a Twitter-like GSS platform with personas derived from the American National Elections Study (ANES), we ablate three layers of partisan identity: affective polarization markers, party identification, and vote choice, while retaining demographics, ideological positioning and important issues. We find that collective outcomes depend heavily on how survey responses are translated into personas. Removing a few explicitly partisan statements moves the network from near-complete segregation to cross-party ties approaching those of real Twitter networks. We argue that matching one outcome is not evidence for behavioral validity and point to salience as a possible explanation. Surveys ask for a position on every question regardless of its importance to the respondent, and persona construction turns each answer into a direct behavioral instruction. We conclude with a checklist for more comprehensive evaluation of personas in future works.

cs.SI↗

arXiv2026

Elite political incivility is rising across democracies

Is political incivility rising across democracies - and if so, why? Analysing approximately 13.8 million tweets from parliamentarians in 26 countries with a validated large language model, we find that the share of severe incivility nearly doubled between 2017 and 2022, from $\approx$2.7\% to $\approx$5.8\% of tweets. Populism is the strongest party-level predictor (OR~=~1.46, 95\%~CI: 1.29--1.65), and its association with incivility is amplified in opposition. Stronger liberal democracy is associated with lower severe incivility (OR~=~0.84 per SD, 95\%~CI: 0.72--0.98). The rise in incivility is overwhelmingly behavioral - party families across the spectrum becoming more aggressive - rather than compositional: the growing prominence of radical parties explains little of it. Nationalist and radical-right parties remain the most uncivil throughout, but the gap narrows as their 2020 peak partially recedes while mainstream parties become steadily more uncivil. Together, these findings point to a mainstreaming of incivility: aggressive and demeaning rhetoric is becoming increasingly common across the political spectrum, with implications for public trust and democratic resilience.

cs.CL↗

arXiv2026

Meeting the Coming Wave: The Emerging Politics of AI and Work across 33 Parliaments

A new politics of artificial intelligence and work is taking shape across party systems, but comparative politics has yet to map it. Using 1,514,950 parliamentary speeches from 33 parliaments (2023-2026), we show this politics follows a different logic than political economy expects. Research anticipates that technological disruption generates demands for compensation; instead, compensation accounts for just 2.3% of response-frame mentions, while enablement and investment dominate (55.2%), regulation and restriction follow (21.8%), and training (20.6%) appears at similar rates across families. Parties disagree instead over what AI means for work and how far this technology should be restrained. The mainstream and radical right support unrestricted enablement; the left is critical but divided on remedy. Social democrats stay adoption-oriented; greens split evenly. The radical left is the clearest force for restriction. The AI conflict thus concerns not compensation after disruption, but whether politics should enable technological change or govern its trajectory.

cs.CY↗

arXiv2026

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation

Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring. Across 19 LLMs and two experiments totaling more than 4 million annotation judgments, we show that subtle identity cues embedded in text systematically bias annotation outcomes in ways that mirror racial stereotypes. In a names-based experiment spanning 39 annotation tasks, texts containing names associated with Black individuals are rated as more aggressive by 18 of 19 models and more gossipy by 18 of 19. Asian names produce a bamboo-ceiling profile: 17 of 19 models rate individuals as more intelligent, while 18 of 19 rate them as less confident and less sociable. Arab names elicit cognitive elevation alongside interpersonal devaluation, and all four minority groups are consistently rated as less self-disciplined. In a matched dialect experiment, the same sentence is judged significantly less professional (all 19 models, mean gap $-0.774$), less indicative of an educated speaker ($-0.688$), more toxic (18/19), and more angry (19/19) when written in African American Vernacular English rather than Standard American English. A notable exception occurs for name-based hireability, where fine-tuning appears to overcorrect, systematically favoring minority-named applicants. These findings suggest that using LLMs as automated annotators can embed socially patterned biases directly into the datasets and measurements that increasingly underpin research, governance, and decision-making.

cs.CL↗

arXiv2026

Political Sorting Can Drive AI Models Apart Through User Feedback

Large language models are rapidly becoming an important source of political information. This raises a fundamental question: will AI systems support a shared basis for political knowledge, or lead different political groups to rely on increasingly different models? Political sorting can drive model fragmentation if three conditions hold: politically different users select into different models, learning from user feedback pushes those models apart politically, and the resulting differences shape subsequent model choices. We call this self-reinforcing process the Centrifugal Alignment Spiral. We study its components in three steps. First, we draw on a human experiment showing that political identity predicts model choice. Second, we fine-tune language models on synthetic feedback reflecting predominantly Democratic or Republican preferences. Across five independent runs per model family, paired models diverged on 12-41% of unseen survey questions with large partisan gaps, and in every run the differences moved in the expected political direction; for some models, differentiation extended even to issue areas excluded from training. Pooling feedback across groups instead suppressed divergence. Third, an empirically anchored agent-based model shows what follows when political sorting and model adaptation operate together: models attract politically distinct audiences, learn from them, and diverge further. User feedback can therefore turn political sorting among AI users into durable differences between the models on which they rely for political information.

cs.CY↗

arXiv2026

Measuring Negative Campaigning across Languages with Large Language Models: A Study of 18 Million Tweets in 19 Countries

Negative campaigning is a defining feature of electoral competition, yet comparative research on its drivers has remained limited by the high cost and limited scalability of existing classification methods. This study makes two key contributions. First, it evaluates zero-shot large language models (LLMs) as a scalable method for cross-lingual classification of negative campaigning. Using benchmark datasets in ten languages, we show that LLM classifications closely match native-speaker human annotations while outperforming conventional supervised models. Second, we leverage this approach to conduct, to our knowledge, the largest cross-national study of negative campaigning to date, analyzing 18 million tweets posted by parliamentarians in 19 European countries between 2017 and 2022. Building on a strategic incentives framework, we argue that governing and coalition-oriented parties face stronger reputational constraints against negative campaigning, whereas opposition and outsider parties face weaker constraints. We further expect parties located away from the ideological center, especially on the radical right, to rely more heavily on confrontational rhetoric. The results support these expectations: cabinet parties are less negative, while ideologically non-centrist parties--most notably radical right parties--are substantially more negative. The study thus provides new comparative evidence on the party-level foundations of campaign negativity while demonstrating how LLMs can transform the scale, consistency, and replicability of research on political discourse across languages and institutional contexts.

cs.CL↗

arXiv2026

The Politics Attention Makes: Platform Media Logic and the Mediatization of Politics

Empirical research on social media and politics has primarily treated platforms as distributive systems that expose users to particular messages. The mediatization literature, however, suggests shifting attention upstream: from circulation to production. Under intense competition for platform attention, political actors who depend on visibility face pressure to learn from recurrent differences in reach and engagement - shaping politics around platform media logic. This paper examines that production-side dimension of platforms political impact by introducing attention price analysis: an exploratory method for estimating the differentiated attention returns associated with forms of expression. Using RoBERTa reward models trained on residualized engagement across X/Twitter, Bluesky, and Mastodon, the analysis compares how platform environments reward rhetorical, emotional, epistemic, and relational features of public communication. The attention signal differs sharply across platforms and engagement actions. X/Twitter sharing rewards antagonism while penalizing respect and nuance; Bluesky reposting favors neutral, lower-emotion language; and Mastodon boosts reward reasoning, nuance, compassion, and collective expression. Toxicity is rewarded across platforms, but in bounded and nonlinear ways. The findings suggest that moving from X/Twitter to less engagement-optimized alternatives such as Bluesky and Mastodon does not eliminate attention pressures, but it may reward less antagonistic and more deliberative forms of politics. The paper contributes a production-side approach to social media and politics by making one dimension of platform media logic empirically visible.

cs.SI↗

arXiv2026

Prompt Stability Scoring for Text Annotation with Large Language Models

Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a given output according to a set of instructions. The reproducibility of LM outputs may nonetheless be vulnerable to small changes in the prompt design. This calls into question the replicability of classification routines. To tackle this problem, researchers have typically tested a variety of semantically similar prompts to determine what we call ``prompt stability." These approaches remain ad-hoc and task specific. In this article, we propose a general framework for diagnosing prompt stability by adapting traditional approaches to intra- and inter-coder reliability scoring. We call the resulting metric the Prompt Stability Score (PSS) and provide a Python package \texttt{promptstability} for its estimation. Using six different datasets and twelve outcomes, we classify $\sim$3.1m rows of data and $\sim$300m input tokens to: a) diagnose when prompt stability is low; and b) demonstrate the functionality of the package. We conclude by providing best practice recommendations for applied researchers.

cs.CL↗

arXiv2026

Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor

Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier models on the political left. A parallel literature shows that LLMs are sycophantic: they adapt their answers to the views, identities, and expectations of the user. We show that these findings are linked: standard political-bias audits partly capture sycophantic accommodation to the inferred auditor. We employ a factorial experiment across three major audit instruments--the Political Compass Test, the Pew Political Typology, and 1,540 partisan-benchmarked Pew American Trends Panel items--administered to six frontier LLMs while varying only the asker's stated identity (N = 30,990 responses). At baseline, all six models lean left. When the asker identifies as a conservative Republican, responses shift sharply: the share of items closer to Democrats falls by 28-62 percentage points, and all six models move right of center. A mirror-image progressive-Democrat cue produces little change; rightward accommodation is 8.0$\times$ larger than leftward. When asked who the default asker is, models identify an auditor, researcher, or academic; when asked what answer that asker expects, they select the Democrat-coded option 75% of the time, nearly the rate under an explicit progressive cue. These patterns are inconsistent with a purely fixed model ideology and indicate that single-prompt audits capture an interaction between model and inferred interlocutor. Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile that must be mapped across realistic interlocutors.

cs.AI↗

arXiv2026

Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation

Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these tasks remains poorly understood: which biases are robust across providers and platforms, and which can be mitigated through prompt design. We present a controlled simulation study mapping content selection biases across three major LLM providers (OpenAI, Anthropic, Google) on real social media datasets from Twitter/X, Bluesky, and Reddit, using six prompting strategies (\textit{general}, \textit{popular}, \textit{engaging}, \textit{informative}, \textit{controversial}, \textit{neutral}). Through 540,000 simulated top-10 selections from pools of 100 posts across 54 experimental conditions, we find that biases differ substantially in how structural and how prompt-sensitive they are. Polarization is amplified across all configurations, toxicity handling shows a strong inversion between engagement- and information-focused prompts, and sentiment biases are predominantly negative. Provider comparisons reveal distinct trade-offs: GPT-4o Mini shows the most consistent behavior across prompts; Claude and Gemini exhibit high adaptivity in toxicity handling; Gemini shows the strongest negative sentiment preference. On Twitter/X, where author demographics can be inferred from profile bios, political leaning bias is the clearest demographic signal: left-leaning authors are systematically over-represented despite right-leaning authors forming the pool plurality in the dataset, and this pattern largely persists across prompts.

cs.SI↗

arXiv2025

Computational Turing Test Reveals Systematic Differences Between Human and AI Language

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption remains largely untested. Existing validation efforts rely heavily on human-judgment-based evaluations -- testing whether humans can distinguish AI from human output -- despite evidence that such judgments are blunt and unreliable. As a result, the field lacks robust tools for assessing the realism of LLM-generated text or for calibrating models to real-world data. This paper makes two contributions. First, we introduce a computational Turing test: a validation framework that integrates aggregate metrics (BERT-based detectability and semantic similarity) with interpretable linguistic features (stylistic markers and topical patterns) to assess how closely LLMs approximate human language within a given dataset. Second, we systematically compare nine open-weight LLMs across five calibration strategies -- including fine-tuning, stylistic prompting, and context retrieval -- benchmarking their ability to reproduce user interactions on X (formerly Twitter), Bluesky, and Reddit. Our findings challenge core assumptions in the literature. Even after calibration, LLM outputs remain clearly distinguishable from human text, particularly in affective tone and emotional expression. Instruction-tuned models underperform their base counterparts, and scaling up model size does not enhance human-likeness. Crucially, we identify a trade-off: optimizing for human-likeness often comes at the cost of semantic fidelity, and vice versa. These results provide a much-needed scalable framework for validation and calibration in LLM simulations -- and offer a cautionary note about their current limitations in capturing human communication.

cs.CL↗

arXiv2025

Shifts in U.S. Social Media Use, 2020-2024: Decline, Fragmentation, and Enduring Polarization

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper traces how the U.S. social media landscape has shifted across platforms, demographics, and politics. Overall platform use has declined, with the youngest and oldest Americans increasingly abstaining from social media altogether. Facebook, YouTube, and Twitter/X have lost ground, while TikTok and Reddit have grown modestly, reflecting a more fragmented digital public sphere. Platform audiences have aged and become slightly more educated and diverse. Politically, most platforms have moved toward Republican users while remaining, on balance, Democratic-leaning. Twitter/X has experienced the sharpest shift: posting has flipped nearly 50 percentage points from Democrats to Republicans. Across platforms, political posting remains tightly linked to affective polarization, as the most partisan users are also the most active. As casual users disengage and polarized partisans remain vocal, the online public sphere grows smaller, sharper, and more ideologically extreme.

cs.CY↗

arXiv2025

Online Homogeneity Can Emerge Without Filtering Algorithms or Homophily Preferences

Ideologically homogeneous online environments - often described as "echo chambers" or "filter bubbles" - are widely seen as drivers of polarization, radicalization, and misinformation. A central debate asks whether such homophily stems primarily from algorithmic curation or users' preference for like-minded peers. This study challenges that view by showing that homogeneity can emerge in the absence of both filtering algorithms and user preferences. Using an agent-based model inspired by Schelling's model of residential segregation, we demonstrate that weak individual preferences, combined with simple group-based interaction structures, can trigger feedback loops that drive communities toward segregation. Once a small imbalance forms, cascades of user exits and regrouping amplify homogeneity across the system. Counterintuitively, algorithmic filtering - often blamed for "filter bubbles" - can in fact sustain diversity by stabilizing mixed communities. These findings highlight online polarization as an emergent system-level dynamic and underscore the importance of applying a complexity lens to the study of digital public spheres.

cs.SI↗

arXiv2025

Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation

Social media platforms have been widely linked to societal harms, including rising polarization and the erosion of constructive debate. Can these problems be mitigated through prosocial interventions? We address this question using a novel method - generative social simulation - that embeds Large Language Models within Agent-Based Models to create socially rich synthetic platforms. We create a minimal platform where agents can post, repost, and follow others. We find that the resulting following-networks reproduce three well-documented dysfunctions: (1) partisan echo chambers; (2) concentrated influence among a small elite; and (3) the amplification of polarized voices - creating a 'social media prism' that distorts political discourse. We test six proposed interventions, from chronological feeds to bridging algorithms, finding only modest improvements - and in some cases, worsened outcomes. These results suggest that core dysfunctions may be rooted in the feedback between reactive engagement and network growth, raising the possibility that meaningful reform will require rethinking the foundational dynamics of platform architecture.

cs.SI↗

arXiv2025

Emergent LLM behaviors are observationally equivalent to data leakage

Ashery et al. recently argue that large language models (LLMs), when paired to play a classic "naming game," spontaneously develop linguistic conventions reminiscent of human social norms. Here, we show that their results are better explained by data leakage: the models simply reproduce conventions they already encountered during pre-training. Despite the authors' mitigation measures, we provide multiple analyses demonstrating that the LLMs recognize the structure of the coordination game and recall its outcomes, rather than exhibit "emergent" conventions. Consequently, the observed behaviors are indistinguishable from memorization of the training corpus. We conclude by pointing to potential alternative strategies and reflecting more generally on the place of LLMs for social science models.

cs.CL↗

arXiv2025

Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations

Recent advancements in AI have reinvigorated Agent-Based Models (ABMs), as the integration of Large Language Models (LLMs) has led to the emergence of ``generative ABMs'' as a novel approach to simulating social systems. While ABMs offer means to bridge micro-level interactions with macro-level patterns, they have long faced criticisms from social scientists, pointing to e.g., lack of realism, computational complexity, and challenges of calibrating and validating against empirical data. This paper reviews the generative ABM literature to assess how this new approach adequately addresses these long-standing criticisms. Our findings show that studies show limited awareness of historical debates. Validation remains poorly addressed, with many studies relying solely on subjective assessments of model `believability', and even the most rigorous validation failing to adequately evidence operational validity. We argue that there are reasons to believe that LLMs will exacerbate rather than resolve the long-standing challenges of ABMs. The black-box nature of LLMs moreover limit their usefulness for disentangling complex emergent causal mechanisms. While generative ABMs are still in a stage of early experimentation, these findings question of whether and how the field can transition to the type of rigorous modeling needed to contribute to social scientific theory.

cs.MA↗

arXiv2024

Toxic Bias: Perspective API Misreads German as More Toxic

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

cs.SI↗

arXiv2024

Best Practices for Text Annotation with Large Language Models

Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in recent months. However, the rapid growth of the field has meant that LLM-based annotation has become something of an academic Wild West: the lack of established practices and standards has led to concerns about the quality and validity of research. Researchers have warned that the ostensible simplicity of LLMs can be misleading, as they are prone to bias, misunderstandings, and unreliable results. Recognizing the transformative potential of LLMs, this paper proposes a comprehensive set of standards and best practices for their reliable, reproducible, and ethical use. These guidelines span critical areas such as model selection, prompt engineering, structured prompting, prompt stability analysis, rigorous model validation, and the consideration of ethical and legal implications. The paper emphasizes the need for a structured, directed, and formalized approach to using LLMs, aiming to ensure the integrity and robustness of text annotation practices, and advocates for a nuanced and critical engagement with LLMs in social scientific research.

cs.CL↗