Hasil untuk "Labor in politics. Political activity of the working class"

Antoine Vendeville, Hiroki Yamashita, Pedro Ramaciotti

Does polarization online reflect the state of polarization in society? We study ideological positions and attitudes on several issues in France, a country with documented issue nonalignment. We compare distributions on X/Twitter with a nationally representative sample, focusing on two key properties: ideological polarization and issue alignment. Despite significant issue-wise divergences, positions of both the X population and the nationally representative sample present a similar bi-dimensional structure along two dominant bundles of aligned issues: a Left-Right divide, and a Global-Local divide. We then study how our results vary when accounting for key structural parameters of the online public sphere: activity, popularity, and visibility. We find that the dimensionality of attitude distributions shrinks as ideological polarization increases when selecting more active users. The divergence between political attitudes on social media and in survey data is greatly mediated by the combination of activity and popularity of social media users: users benefiting from the most exposure are also the most representative of the general public. Together, our results shed light on the structural similarities and differences between political attitudes from social media users and the general public.

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Rakesh Thakur, Sneha Sharma, Gauri Chopra

Fact-checking in code-mixed, low-resource languages such as Hinglish remains an underexplored challenge in natural language processing. Existing fact-verification systems largely focus on high-resource, monolingual settings and fail to generalize to real-world political discourse in linguistically diverse regions like India. Given the widespread use of Hinglish by public figures, particularly political figures, and the growing influence of social media on public opinion, there's a critical need for robust, multilingual and context-aware fact-checking tools. To address this gap a novel benchmark HiFACT dataset is introduced with 1,500 realworld factual claims made by 28 Indian state Chief Ministers in Hinglish, under a highly code-mixed low-resource setting. Each claim is annotated with textual evidence and veracity labels. To evaluate this benchmark, a novel graphaware, retrieval-augmented fact-checking model is proposed that combines multilingual contextual encoding, claim-evidence semantic alignment, evidence graph construction, graph neural reasoning, and natural language explanation generation. Experimental results show that HiFACTMix outperformed accuracy in comparison to state of art multilingual baselines models and provides faithful justifications for its verdicts. This work opens a new direction for multilingual, code-mixed, and politically grounded fact verification research.

Sujit Mandava, Deepak P, Sahely Bhadra

While there has been much research into developing AI techniques for fake news detection aided by various benchmark datasets, it has often been pointed out that fake news in different geo-political regions traces different contours. In this work we uncover, through analytical arguments and empirical evidence, the existence of an important characteristic in news originating from the Global South viz., the geo-political veracity gradient. In particular, we show that Global South news about topics from Global North -- such as news from an Indian news agency on US elections -- tend to be less likely to be fake. Observing through the prism of the political economy of fake news creation, we posit that this pattern could be due to the relative lack of monetarily aligned incentives in producing fake news about a different region than the regional remit of the audience. We provide empirical evidence for this from benchmark datasets. We also empirically analyze the consequences of this effect in applying AI-based fake news detection models for fake news AI trained on one region within another regional context. We locate our work within emerging critical scholarship on geo-political biases within AI in general, particularly with AI usage in fake news identification; we hope our insight into the geo-political veracity gradient could help steer fake news AI scholarship towards positively impacting Global South societies.

Arthur Capozzi

Political advertising on social media has fundamentally reshaped democratic deliberation, playing a central role in electoral campaigns and propaganda. However, its systemic impact remains largely theoretical or unexplored, raising critical concerns about institutional fairness and algorithmic transparency. This paper provides the first data-driven analysis of the relationship between direct democracy and political advertising on social media, leveraging a novel dataset of 40,000 political ads published on Meta in Switzerland between 2021 and 2025. Switzerland's system of direct democracy, characterized by frequent referenda, provides an ideal context for examining this relationship beyond standard electoral cycles. The results reveal the sheer scale of digital campaigning, with 560 million impressions targeting 5.6 million voters, and suggest that greater exposure to "pro-Yes" advertising significantly correlates with referendum approval outcomes. Demographic microtargeting analysis suggests partisan strategies: Centrist and right-wing parties predominantly target older men, whereas left-wing parties focus on young women. Regarding textual content, a clear pattern of "talking past each other" is identified; in line with the issue ownership theory, parties avoid debating shared issues, preferring to promote exclusively owned topics. Furthermore, the parties' strategies are so distinctive that a machine learning model trained only on audience and topic features can accurately predict the author of an advertisement. This article highlights how demographic microtargeting, issue divergence, and tailored messages could undermine democratic deliberation, exposing a paradox: Referenda are designed to be the ultimate expression of the popular will, yet they are highly susceptible to invisible algorithmic persuasion.

Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra

The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Language Models (LLMs) for classifying the political ideology of online content through in-context learning (ICL). Our extensive experiments involving demonstration selection in label-balanced fashion, conducted on three datasets comprising news articles and YouTube videos, reveal that our approach significantly outperforms zero-shot and traditional supervised methods. Additionally, we evaluate the influence of metadata (e.g., content source and descriptions) on ideological classification and discuss its implications. Finally, we show how providing the source for political and non-political content influences the LLM's classification.

Rafael Acuna, Aldie Alejandro, Robert Leung

Dynasties have long dominated Philippine politics. Despite the theoretical consensus that dynastic rule erodes democratic accountability, there is limited empirical evidence establishing dynasties' true impact on development. A key challenge has been developing robust metrics for characterizing dynasties that facilitate meaningful comparisons across geographies and election cycles. Using election data from 2004 to 2022, we leverage methods from graph theory to develop four indicators to investigate dynastic evolution: Political Herfindahl-Hirschman Index (HHI), measuring dynastic power concentration; Centrality Gini Coefficient (CGC), reflecting inequalities of influence between clan members; Connected Component Density (CCD), representing the degree of inter-clan connection; and Average Community Connectivity (ACC), quantifying intra-clan cohesion. Our analysis reveals three key findings. Firstly, dynasties have grown stronger and more interconnected, occupying an increasing share of elected positions. Dominant clans have also remained tightly knit, but with great power imbalances between members. Secondly, we examine variations in party-hopping between dynastic and non-dynastic candidates. Across every election cycle, party-hopping rates are significantly higher (p<0.01) among dynastic candidates than non-dynasts, suggesting that the dominance of dynasties may weaken institutional trust within parties. Finally, applying a Linear Mixed Model regression, controlling for geographic random-effects and time fixed-effects, we observe that provinces with high power asymmetries within clans (high CGCs) and with deeply interconnected clans (high CCDs) record significantly lower (p<0.05) Human Development Index scores. These findings suggest that clan structure, rather than power concentration alone--may be the chief determinant of a ruling dynasty's developmental impact.

Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan et al.

Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts and expressed through particular uses of language, existing datasets for group and identity detection are predominantly English-centric, single-label and focus on coarse identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection: 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), manually annotated for twelve nuanced social identities (e.g. Rightist, Ultra-Orthodox, Socially-oriented) grounded by survey data. We benchmark multilabel and single-label encoders alongside 2B-9B-parameter generative LLMs, finding that Hebrew-tuned LLMs provide the best results (macro-$F_1$ = 0.74). We apply our classifier to politicians' Facebook posts and parliamentary speeches, evaluating differences in popularity, temporal trends, clustering patterns, and gender-related variations in identity expression. We utilize identity choices from a national public survey, enabling a comparison between identities portrayed in elite discourse and the public's identity priorities. HebID provides a comprehensive foundation for studying social identities in Hebrew and can serve as a model for similar research in other non-English political contexts.

Flávia Fernanda Santos Silva, Geliana Cardoso de Lima, Ricardo Alves Januário

Sabemos que há um conjunto de manifestações de matriz africana e indígena no Brasil que necessitam ser reconhecidas por seus valores culturais e educativos. Com base nessa realidade, este artigo tem por objetivo apresentar os resultados de um Projeto de Extensão, realizado na Universidade Federal do Amazonas. Para a realização do projeto, foi oferecido um encontro de capacitação para os professores(as) de uma escola municipal, promovendo discussões teóricas, e realização de oficinas, de modo que posteriormente fossem executadas atividades nas escolas. Foram suscitados debates, assim como o reconhecimento e a efetivação de atividades de planejamento sobre essa temática no contexto escolar, buscando o fortalecimento do trabalho numa perspectiva interdisciplinar, além de trocas de experiências nessa relação escola/comunidade acadêmica, combatendo os estereótipos negativos e os preconceitos que se perpetuam na sociedade brasileira e amazonense em torno da cultura afro-brasileira e indígena.

Lucas Andrade de Morais, Maria das Graças Duarte de Andrade Neta, Luan Caio Andrade de Morais

Realizado por meio de uma revisão integrativa da literatura no período de 2013 a 2023, nas plataformas de periódicos Capes e BVS, este estudo busca identificar a relação entre educação popular e controle social na saúde, focando nos Conselhos municipais de saúde como agentes fundamentais. Inspirada nos princípios de Paulo Freire, a educação popular emerge como perspectiva prático-teórica para empoderar conselheiros e comunidade, impulsionando uma participação efetiva no processo de aprendizagem. A literatura revisada destaca a relevância dos Conselhos, mas alerta para distanciamentos e ênfase excessiva em questões administrativas. A educação popular surge como prática transformadora, capacitando atores e fortalecendo a participação ativa. Seus princípios, aplicados nos Conselhos, contribuem para uma atuação mais crítica, eficaz e democrática. Este estudo sugere a necessidade de abordagens participativas e emancipatórias para fortalecer o Sistema Único de Saúde.

Svetlana Churina, Kokil Jaidka

Incivility on platforms such as Twitter (now X) and Reddit complicates the development of AI systems that can support productive, rhetorically sound political argumentation. We present experiments with \textit{GPT-3.5 Turbo} fine-tuned on two contrasting datasets of political discourse: high-incivility Twitter replies to U.S. Congress and low-incivility posts from Reddit's \textit{r/ChangeMyView}. Our evaluation examines how data composition and prompting strategies affect the rhetorical framing and deliberative quality of model-generated arguments. Results show that Reddit-finetuned models generate safer but rhetorically rigid arguments, while cross-platform fine-tuning amplifies adversarial tone and toxicity. Prompt-based steering reduces overt toxicity (e.g., personal attacks) but cannot fully offset the influence of noisy training data. We introduce a rhetorical evaluation rubric - covering justification, reciprocity, alignment, and authority - and provide implementation guidelines for authoring, moderation, and deliberation-support systems.

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Rabeya Islam Mumu et al.

Sentiment analysis is the process of identifying and categorizing people's emotions or opinions regarding various topics. Analyzing political sentiment is critical for understanding the complexities of public opinion processes, especially during election seasons. It gives significant information on voter preferences, attitudes, and current trends. In this study, we investigate political sentiment analysis during Bangladeshi elections, specifically examining how effectively Pre-trained Language Models (PLMs) and Large Language Models (LLMs) capture complex sentiment characteristics. Our study centers on the creation of the "Motamot" dataset, comprising 7,058 instances annotated with positive and negative sentiments, sourced from diverse online newspaper portals, forming a comprehensive resource for political sentiment analysis. We meticulously evaluate the performance of various PLMs including BanglaBERT, Bangla BERT Base, XLM-RoBERTa, mBERT, and sahajBERT, alongside LLMs such as Gemini 1.5 Pro and GPT 3.5 Turbo. Moreover, we explore zero-shot and few-shot learning strategies to enhance our understanding of political sentiment analysis methodologies. Our findings underscore BanglaBERT's commendable accuracy of 88.10% among PLMs. However, the exploration into LLMs reveals even more promising results. Through the adept application of Few-Shot learning techniques, Gemini 1.5 Pro achieves an impressive accuracy of 96.33%, surpassing the remarkable performance of GPT 3.5 Turbo, which stands at 94%. This underscores Gemini 1.5 Pro's status as the superior performer in this comparison.

Zhenyu Wang, Dequan Wang, Yi Xu et al.

The recent wave of artificial intelligence, epitomized by large language models (LLMs),has presented opportunities and challenges for methodological innovation in political science,sparking discussions on a potential paradigm shift in the social sciences. However, how can weunderstand the impact of LLMs on knowledge production and paradigm transformation in thesocial sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political scienceresearch? These questions, particularly from a practical methodological standpoint, remainunderexplored. This paper proposes the "Intelligent Computing Social Modeling" (ICSM) methodto address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages thestrengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration inpolitical science through "simulated social construction" and "simulation validation." Bysimulating the U.S. presidential election, this study empirically demonstrates the operationalpathways and methodological advantages of ICSM. By integrating traditional social scienceparadigms, ICSM not only enhances the quantitative paradigm's capability to apply big data toassess the impact of factors but also provides qualitative paradigms with evidence for socialmechanism discovery at the individual level, offering a powerful tool that balances interpretabilityand predictability in social science research. The findings suggest that LLMs will drivemethodological innovation in political science through integration and improvement rather thandirect substitution.

Nicholas Deas, Kathleen McKeown

Global partisan hostility and polarization has increased, and this polarization is heightened around presidential elections. Models capable of generating accurate summaries of diverse perspectives can help reduce such polarization by exposing users to alternative perspectives. In this work, we introduce a novel dataset and task for independently summarizing each political perspective in a set of passages from opinionated news articles. For this task, we propose a framework for evaluating different dimensions of perspective summary performance. We benchmark 11 summarization models and LLMs of varying sizes and architectures through both automatic and human evaluation. While recent models like GPT-4o perform well on this task, we find that all models struggle to generate summaries that are faithful to the intended perspective. Our analysis of summaries focuses on how extraction behavior is impacted by features of the input documents.

Daniel Menezes Lovisi, André Campos Machado, Carlos Roberto Ferreira de Menezes Júnior

Este texto tem como objetivo compartilhar parte das experiências vivenciadas no curso de extensão online Ritmos Brasileiros para Violão, realizado em quatro edições entre os anos de 2020 e 2022, como iniciativa de três docentes do curso de Graduação em Música da Universidade Federal de Uberlândia. Pretende-se, aqui, fazer um breve histórico das ações propostas, refletir sobre a forma de elaboração do projeto e seu impacto junto aos participantes. O presente relato também serve como forma de avaliar o trabalho e de se pensar em possíveis desdobramentos da atividade extensionista em um momento pós-pandemia.

Ernanda Mezaroba, Marja Camargo Garcia, Barbara Rodrigues Araujo et al.

O objetivo deste texto é relatar a experiência da vigilância do crescimento e do desenvolvimento de crianças com até vinte e quatro meses de idade, em uma Estratégia de Saúde da Família. Trata-se de um relato de experiência sobre ações voltadas à saúde infantil, realizadas em um município no interior do Rio Grande do Sul. A iniciativa se deu a partir de inquietações quanto ao cuidado fragmentado, ao monitoramento insatisfatório de crianças e à necessidade de promover mudanças na comunidade. A interdisciplinaridade, pelo seu potencial em consolidar atenção integral e práticas mais resolutivas, foi o eixo norteador para desenvolver os cuidados às crianças. Essas ações abrangem consulta de puericultura compartilhada, visitas domiciliares e atividades educativas em grupo, com o intuito de, sobretudo, promover a saúde, prevenir doenças, assegurar um processo de crescimento e desenvolvimento exitoso e garantir a longitudinalidade do cuidado. A metodologia de atendimento desenvolvida permitiu realizar reflexões sobre o quão positivo é romper com o pensamento profissional fragmentado, integrando os saberes das diferentes áreas. A busca e a manutenção da atuação interdisciplinar foram desafiadoras e mobilizaram reflexões na equipe continuamente. Ademais, ampliaram, também, as possibilidades e a capacidade de transformar as práticas de atenção à saúde das crianças.

Victor Steinborn, Antonis Maronikolakis, Hinrich Schütze

In efforts to keep up with the rapid progress and use of large language models, gender bias research is becoming more prevalent in NLP. Non-English bias research, however, is still in its infancy with most work focusing on English. In our work, we study how grammatical gender bias relating to politeness levels manifests in Japanese and Korean language models. Linguistic studies in these languages have identified a connection between gender bias and politeness levels, however it is not yet known if language models reproduce these biases. We analyze relative prediction probabilities of the male and female grammatical genders using templates and find that informal polite speech is most indicative of the female grammatical gender, while rude and formal speech is most indicative of the male grammatical gender. Further, we find politeness levels to be an attack vector for allocational gender bias in cyberbullying detection models. Cyberbullies can evade detection through simple techniques abusing politeness levels. We introduce an attack dataset to (i) identify representational gender bias across politeness levels, (ii) demonstrate how gender biases can be abused to bypass cyberbullying detection models and (iii) show that allocational biases can be mitigated via training on our proposed dataset. Through our findings we highlight the importance of bias research moving beyond its current English-centrism.

JS Tan, Natalia Luka, Emily Mazo

Tech workers -- professional workers in the technology industry including software engineers, product managers, UX designers, etc. -- are not normally associated with labor activism. Yet, since 2017, we have seen a significant rise in labor actions among this group. Using an original dataset, we demonstrate how, in the case of tech workers, periods of intense workplace social activism preceded later periods of heightened labor activism. Regression analysis confirms that participation in social activism increases the likelihood of labor activism six months to one year later at the same company. This finding extends Fantasia's cultures of solidarity argument to professional workers. We find that organizing emerges out of collective action and ensuing conflict with management: first, tech workers, guided by their professional interest in socially beneficial work, engage in workplace social activism. This generates solidarity among employee-participants but also creates conflict with management and leads to the emergence of labor activism among professionals.

Anabella Gluj

El presente artículo tiene por objetivo recuperar la experiencia del sindicalismo norteamericano,  principalmente de la central American Federation of Labor and Congress of Industrial Organizations (AFL-CIO), en su intervención en el marco del movimiento antiglobalización. Se propone un análisis de su participación en la llamada “Batalla de Seattle” ante la Conferencia de la Organización Mundial de Comercio (OMC) en 1999 y, luego, su posterior protagonismo en la campaña de rechazo del ingreso de China a dicho organismo internacional. Especialmente, el trabajo focaliza en los límites y alcances que tuvo este fenómeno, indagando en la relación entre los sindicatos y el Partido Demócrata, en aquel entonces bajo la presidencia de Clinton.

Pedro Pereira do Nascimento, Alexandre dos Santos Rocha

Na praia de Uruaú, a pesca artesanal tem grande influência no mantimento econômico da comunidade, expandindo-se para outras práticas culturais. Uma delas é a confecção de jangadinhas, pequenas embarcações que simulam as jangadas utilizadas pelos pescadores. Essas jangadinhas são utilizadas por pescadores e filhos de pescadores em brincadeiras na praia e nas lagoas da comunidade. Partimos da problemática de como acontece o processo de inserção na atividade da pesca, pensando a brincadeira com as jangadinhas como parte desse processo, trazendo o debate da relação do trabalho e os movimentos sociais na formação do conceito de educação popular, destacando aspectos da pesca artesanal em Uruaú e sua organização social. As observações para este trabalho foram iniciadas em 2016, referentes ao movimento dos pescadores e suas pautas de mantimento das suas práticas na lagoa do Uruaú, junto a uma inserção na Colônia Z-11, que cuida das questões políticas e sociais dos pescadores de Beberibe, no ano de 2017, promovendo observações dentro da instituição que rege o movimento.