Arman Engin Sucu, Yixiang Zhou, Mario A. Nascimento
et al.
This study investigates the use of Large Language Models (LLMs) for political stance detection in informal online discourse, where language is often sarcastic, ambiguous, and context-dependent. We explore whether providing contextual information, specifically user profile summaries derived from historical posts, can improve classification accuracy. Using a real-world political forum dataset, we generate structured profiles that summarize users' ideological leaning, recurring topics, and linguistic patterns. We evaluate seven state-of-the-art LLMs across baseline and context-enriched setups through a comprehensive cross-model evaluation. Our findings show that contextual prompts significantly boost accuracy, with improvements ranging from +17.5\% to +38.5\%, achieving up to 74\% accuracy that surpasses previous approaches. We also analyze how profile size and post selection strategies affect performance, showing that strategically chosen political content yields better results than larger, randomly selected contexts. These findings underscore the value of incorporating user-level context to enhance LLM performance in nuanced political classification tasks.
Gabriela Juncosa, Saeedeh Mohammadi, Margaret Samahita
et al.
Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Footnotes on TikTok, and Facebook's Community Notes initiative. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesize that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance their overall quality. To test this idea, we conducted an online experiment in which participants jointly authored notes on political posts. Our results show that teams produce notes that are rated as more helpful than individually written notes. We also find that politically diverse teams perform better when evaluating Republican posts, while group composition does not affect perceived note quality for Democrat posts. However, the advantage of collaboration diminishes when team members are aware of one another's political affiliations. Taken together, these findings underscore the complexity of community-based content moderation and highlight the importance of understanding group dynamics and political diversity when designing more effective moderation systems.
Political moderation, a key attractor in democratic systems, proves highly fragile under realistic information conditions. We develop a stochastic model of opinion dynamics to analyze how noise and differential susceptibility reshape the political spectrum. Extending Marvel et al.'s deterministic framework, we incorporate stochastic media influence $ζ(t)$ and neuropolitically-grounded sensitivity differences ($σ_y > σ_x$). Analysis reveals the moderate population -- stable in deterministic models -- undergoes catastrophic collapse under stochastic forcing. This occurs through an effective deradicalization asymmetry ($u_{B}^{\text{eff}} = u + σ_y^2/2 > u_{A}^{\text{eff}}$) that drives conservatives to extinction, eliminating cross-cutting interactions that sustain moderates. The system exhibits a phase transition from multi-stable coexistence to liberal dominance, demonstrating how information flow architecture -- independent of content -- systematically dismantles the political center. Our findings reveal moderation as an emergent property highly vulnerable to stochastic perturbations in complex social systems.
Abstract This article examines the large and modern sugar factories established in Egypt in the 1870s as multi-phased production sites that combined coerced peasant labor with the deployment of state of the art steam technology. These factories possessed the capacity to produce 7.5% of global sugar output from sugarcane at the time. Yet their scale, combined with their reliance on forced labor, have been neglected in Egypt’s labor historiography. The article argues that the forms of resistance enacted by coerced workers on these worksites co-shaped the emergence of peasant wage labor in subsequent decades. Drawing on the analytical perspectives of sugar history and Global Labor History (GLH), it demonstrates how wage labor took shape on industrial worksites in rural regions—challenging earlier labor histories that treat urban wage labor as the starting point of modern Egypt’s labor history. In doing so, it shows that rural labor strikes predated the conventional periodization of labor strikes and working-class formation in Egypt’s cities during the 1880s–90s. Finally, this global microhistory argues that the materiality and temporality of sugar production, when combined with the resources-demanding and labor-intensive technology of the factories, complicate Egypt’s late nineteenth-century position as a commodity frontier for Europe’s industrial capitalism.
Implicit content plays a crucial role in political discourse, where speakers systematically employ pragmatic strategies such as implicatures and presuppositions to influence their audiences. Large Language Models (LLMs) have demonstrated strong performance in tasks requiring complex semantic and pragmatic understanding, highlighting their potential for detecting and explaining the meaning of implicit content. However, their ability to do this within political discourse remains largely underexplored. Leveraging, for the first time, the large IMPAQTS corpus, which comprises Italian political speeches with the annotation of manipulative implicit content, we propose methods to test the effectiveness of LLMs in this challenging problem. Through a multiple-choice task and an open-ended generation task, we demonstrate that all tested models struggle to interpret presuppositions and implicatures. We conclude that current LLMs lack the key pragmatic capabilities necessary for accurately interpreting highly implicit language, such as that found in political discourse. At the same time, we highlight promising trends and future directions for enhancing model performance. We release our data and code at https://github.com/WalterPaci/IMPAQTS-PID
Why have left-wing movements historically integrated participatory art forms (such as murals and protest songs) into their praxis, while right-wing movements have prioritized strategic communication and, more recently, the digital culture of memes? This article introduces the concept of aesthetic asymmetry to explain this divergence in political action. We argue that the asymmetry is not coincidental but the result of four interconnected structural factors: the organizational ecosystem, the moral and emotional framework, the material supports, and the historical tradition of each political spectrum. While the left tends to use art in a constitutive manner to forge community, solidarity, and hope, the contemporary right tends to use it instrumentally to mobilize polarizing affects such as humor and resentment. Drawing on comparative literature from the Theatre of the Oppressed to analyses of alt-right meme wars, we nuance this distinction and show how the aesthetic logic of each pole aligns with its strategic objectives. The article culminates in a prescriptive model for artistic action, synthesizing keys to effective mobilization into emotional, narrative, and formatting strategies. Understanding this asymmetry is crucial for analyzing political communication and for designing cultural interventions capable of generating profound social change.
Despite widespread concerns about the risks of AI-generated content (AIGC) to the integrity of social media discourse, little is known about its scale and scope, the actors responsible for its dissemination online, and the user responses it elicits. In this work, we measure and characterize the prevalence, spreaders, and emotional reception of AI-generated political images. Analyzing a large-scale dataset from Twitter/X related to the 2024 U.S. Presidential Election, we find that approximately 12% of shared images are detected as AI-generated, and around 10% of users are responsible for sharing 80% of AI-generated images. AIGC superspreaders--defined as the users who not only share a high volume of AI-generated images but also receive substantial engagement through retweets--are more likely to be X Premium subscribers, have a right-leaning orientation, and exhibit automated behavior. Their profiles contain a higher proportion of AI-generated images than non-superspreaders, and some engage in extreme levels of AIGC sharing. Moreover, superspreaders' AI image tweets elicit more positive and less toxic responses than their non-AI image tweets. This study serves as one of the first steps toward understanding the role generative AI plays in shaping online socio-political environments and offers implications for platform governance.
W. Russell Neuman, Chad Coleman, Ali Dasdan
et al.
Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically investigates the political temperament of seven prominent LLMs - OpenAI's GPT-4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's Llama 4, Mistral 7b Le Chat and High-Flyer's DeepSeek R1 -- using a multi-pronged approach that includes Moral Foundations Theory, a dozen established political ideology scales and a new index of current political controversies. We find strong and consistent prioritization of liberal-leaning values, particularly care and fairness, across most models. Further analysis attributes this trend to four overlapping factors: Liberal-leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse and safety-driven fine-tuning practices. We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine-tuned model pairs reveals that fine-tuning generally increases liberal lean, an effect confirmed through both self-report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preference of programmers but an emergent property of training on democratic rights-focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil-of ignorance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective reasoning.
Political biases encoded by LLMs might have detrimental effects on downstream applications. Existing bias analysis methods rely on small-size intermediate tasks (questionnaire answering or political content generation) and rely on the LLMs themselves for analysis, thus propagating bias. We propose a new approach leveraging the observation that LLM sentiment predictions vary with the target entity in the same sentence. We define an entropy-based inconsistency metric to encode this prediction variability. We insert 1319 demographically and politically diverse politician names in 450 political sentences and predict target-oriented sentiment using seven models in six widely spoken languages. We observe inconsistencies in all tested combinations and aggregate them in a statistically robust analysis at different granularity levels. We observe positive and negative bias toward left and far-right politicians and positive correlations between politicians with similar alignment. Bias intensity is higher for Western languages than for others. Larger models exhibit stronger and more consistent biases and reduce discrepancies between similar languages. We partially mitigate LLM unreliability in target-oriented sentiment classification (TSC) by replacing politician names with fictional but plausible counterparts.
Alejandro De La Fuente-Cuesta, Alberto Martinez-Serra, Nienke Visscher
et al.
The use of large language models (LLMs) is becoming common in political science and digital media research. While LLMs have demonstrated ability in labelling tasks, their effectiveness to classify Political Content (PC) from URLs remains underexplored. This article evaluates whether LLMs can accurately distinguish PC from non-PC using both the text and the URLs of news articles across five countries (France, Germany, Spain, the UK, and the US) and their different languages. Using cutting-edge models, we benchmark their performance against human-coded data to assess whether URL-level analysis can approximate full-text analysis. Our findings show that URLs embed relevant information and can serve as a scalable, cost-effective alternative to discern PC. However, we also uncover systematic biases: LLMs seem to overclassify centrist news as political, leading to false positives that may distort further analyses. We conclude by outlining methodological recommendations on the use of LLMs in political science research.
This paper investigates the usage patterns of Facebook among different demographics in the United States, focusing on the consumption of political information and its variability across age, gender, and ethnicity. Employing a novel data donation model, we developed a tool that allows users to voluntarily share their interactions with public Facebook groups and pages, which we subsequently enrich using CrowdTangle. This approach enabled the collection and analysis of a dataset comprising over 1,200 American users. Our findings indicate that political content consumption on Facebook is relatively low, averaging around 17%, and exhibits significant demographic variations. Additionally, we provide insights into the temporal trends of these interactions. The main contributions of this research include a methodological framework for studying social media usage in a privacy-preserving manner, a comprehensive dataset reflective of current engagement patterns, and descriptive insights that highlight demographic disparities and trends over time. This study enhances our understanding of social media's role in information dissemination and its implications for political engagement, offering a valuable resource for researchers and policymakers in a landscape where direct data access is diminishing.
El objetivo de este trabajo es analizar la dinámica identitaria de camioneros desde fines del siglo XX hasta la actualidad ampliando los factores explicativos del poder sindical del colectivo más allá del éxito en sus estrategias de adaptación organizativa, el aprovechamiento de las oportunidades políticas y su poder estructural en la economía. Para ello se hará foco en la expansión de la identidad político-sindical, la recurrente y sostenida estrategia de protagonismo en nuevos nucleamientos sindicales y multisectoriales, así como en las pretensiones de restablecer la influencia del sindicalismo en la política. La consideración cualitativa de fuentes secundarias, junto a entrevistas a exdelegados, militantes y dirigentes del gremio, dan sustento al presente artículo.
1789-, Labor in politics. Political activity of the working class
Social media platforms are often blamed for exacerbating political polarization and worsening public dialogue. Many claim that hyperpartisan users post pernicious content, slanted to their political views, inciting contentious and toxic conversations. However, what factors are actually associated with increased online toxicity and negative interactions? In this work, we explore the role that partisanship and affective polarization play in contributing to toxicity both on an individual user level and a topic level on Twitter/X. To do this, we train and open-source a DeBERTa-based toxicity detector with a contrastive objective that outperforms the Google Jigsaw Perspective Toxicity detector on the Civil Comments test dataset. Then, after collecting 89.6 million tweets from 43,151 Twitter/X users, we determine how several account-level characteristics, including partisanship along the US left-right political spectrum and account age, predict how often users post toxic content. Fitting a Generalized Additive Model to our data, we find that the diversity of views and the toxicity of the other accounts with which that user engages has a more marked effect on their own toxicity. Namely, toxic comments are correlated with users who engage with a wider array of political views. Performing topic analysis on the toxic content posted by these accounts using the large language model MPNet and a version of the DP-Means clustering algorithm, we find similar behavior across 5,288 individual topics, with users becoming more toxic as they engage with a wider diversity of politically charged topics.
In this paper, using Monte Carlo simulations we show that the Blume-Capel model gives rise to the social depolarization. This model borrowed from statistical physics uses the continuous Ising spin varying from -1 to 1 passing by zero to express the political stance of an individual going from ultra-left (-1) to ultra-right (+1). The particularity of the Blume-Capel model is the existence of a $D$-term which favors the state of spin zero which is a neutral stance. We consider the political system of the USA where voters affiliate with two political groups: Democrats or Republicans, or are independent. Each group is composed of a large number of interacting members of the same stance. We represent the general political ambiance (or degree of social turmoil) with a temperature $T$ similar to thermal agitation in statistical physics. When three groups interact with each other, their stances can get closer or further from each other, depending on the nature of their inter-group interactions. We study the dynamics of such variations as functions of the value of the $D$-term of each group. We show that the polarization decreases with increasing $D$. We outline the important role of $T$ in these dynamics. These MC results are in excellent agreement with the mean-field treatment of the same model.
Resumen de Jessica Blanco, Historia de una relación impensada. El catolicismo en los sindicatos durante el peronismo, Buenos Aires, Eudem, 2021, 121 pgs.
1789-, Labor in politics. Political activity of the working class
Manoel Júnior, Philipe Melo, Daniel Kansaon
et al.
Instant messaging platforms such as Telegram became one of the main means of communication used by people all over the world. Most of them are home of several groups and channels that connect thousands of people focused on political topics. However, they have suffered with misinformation campaigns with a direct impact on electoral processes around the world. While some platforms, such as WhatsApp, took restrictive policies and measures to attenuate the issues arising from the abuse of their systems, others have emerged as alternatives, presenting little or no restrictions on content moderation or actions in combating misinformation. Telegram is one of those systems, which has been attracting more users and gaining popularity. In this work, we present the "Telegram Monitor", a web-based system that monitors the political debate in this environment and enables the analysis of the most shared content in multiple channels and public groups. Our system aims to allow journalists, researchers, and fact-checking agencies to identify trending conspiracy theories, misinformation campaigns, or simply to monitor the political debate in this space along the 2022 Brazilian elections. We hope our system can assist the combat of misinformation spreading through Telegram in Brazil.
Fabiana Rodrigues da Silva, Airton Rodrigues dos Santos, Vanessa Cláudia Vasconcelos Segundo
et al.
Uma horta constitui uma área, geralmente de pequena extensão, onde pode ser realizada a atividade de cultivo das mais diversas culturas agrícolas, como legumes ou hortaliças, submetidas a um manejo intensivo de produção. O objetivo deste trabalho foi avaliar a implantação e o desenvolvimento de hortas orgânicas em distintos usos didáticos, frente a diferentes níveis de ensino: educação básica e educação superior. Em ambos os locais de implantação das hortas foi realizada a aplicação de questionários e após tabulação desses dados foi possível concluir que, na educação básica, a horta teve como efeito a sensibilização e a educação ambiental. Já para a educação superior serviu como treinamento técnico profissional para os futuros agroecólogos em formação. Portanto, a implantação de hortas é uma metodologia ativa de ensino que pode ser usada em diferentes níveis de ensino e com diferentes objetivos.
Social Sciences, Labor in politics. Political activity of the working class
Stela Cristina de Godoi, Iara Teixeira Rebouças dos Santos, Jaqueline Jordão
Este escrito baseia-se na observação participante obtida pela prática extensionista, junto ao grupo de mulheres “Guerreiras do Satélite”, assistidas pelo CRAS Satélite Íris, no município de Campinas-SP. O projeto que subsidia essa reflexão desenvolve atividades socioeducativas, visando contribuir para o fortalecimento das mulheres e de seus vínculos sociais. As ações são pensadas como estratégias para o enfrentamento do insulamento das mulheres que resulta da desigualdade na divisão sexual do trabalho. Observamos que o recrutamento prematuro das meninas para os trabalhos reprodutivos, o abandono escolar e a violência doméstica privam as mulheres de ocupar espaços sociais fora do núcleo familiar. Neste sentido, as intervenções utilizam Círculos de Cultura, baseados na perspectiva freiriana de educação como prática de comunhão e liberdade, e Círculos de Trabalho Artístico, que concebe a educação como esforços conativos do corpo-mente social em sua busca por preservar-se e atualizar o bem comum. Por meio da atividade problematizadora e de reconstrução imaginativa que culminam na produção artística, buscamos ressignificar o sofrimento, despertar as paixões alegres e fortalecer um devir ético que encorajem essas mulheres a perseverar em si próprias, tecer laços de solidariedade e ocupar outros espaços sociais não prescritos pelas estruturas vigentes.
Social Sciences, Labor in politics. Political activity of the working class
The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from elite politicians in the U.S., we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most of these propositions are inspired by, but inadequately grounded in, political philosophy scholarship. This paper aims to correct that deficit. We introduce a taxonomy of fairness ideals using doctrines of Equality of Opportunity (EOP) from political philosophy, clarifying their conceptions in philosophy and the proposed codification in fair machine learning. We arrange these fairness ideals onto an EOP spectrum, which serves as a useful frame to guide the design of a fair ADS in a given context. We use our fairness-as-EOP framework to re-interpret the impossibility results from a philosophical perspective, as the in-compatibility between different value systems, and demonstrate the utility of the framework with several real-world and hypothetical examples. Through our EOP-framework we hope to answer what it means for an ADS to be fair from a moral and political philosophy standpoint, and to pave the way for similar scholarship from ethics and legal experts.