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

Byunghwee Lee, Sangyeon Kim, Filippo Menczer et al.

Due to the correlational structure in our traits such as identities, cultures, and political attitudes, seemingly innocuous preferences like following a band or using a specific slang can reveal private traits. This possibility, especially when combined with massive, public social data and advanced computational methods, poses a fundamental privacy risk. As our data exposure online and the rapid advancement of AI are increasing the risk of misuse, it is critical to understand the capacity of large language models (LLMs) to exploit such potential. Here, using online discussions on DebateOrg and Reddit, we show that LLMs can reliably infer hidden political alignment, significantly outperforming traditional machine learning models. Prediction accuracy further improves as we aggregate multiple text-level inferences into a user-level prediction, and as we use more politics-adjacent domains. We demonstrate that LLMs leverage words that are highly predictive of political alignment while not being explicitly political. Our findings underscore the capacity and risks of LLMs for exploiting socio-cultural correlates.

Antoine Vendeville, Jimena Royo-Letelier, Duncan Cassells et al.

Studying political activity on social media often requires defining and measuring political stances of users or content. Relevant examples include the study of opinion polarization, or the study of political diversity in online content diets. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general political systems, in which users and media outlets might exhibit stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a dataset pertaining to a population of X/Twitter users, parliamentarians, and media outlets embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment, in addition to left-right and liberal-conservative scales. We include indicators of individual activity and popularity: mean number of posts per day, number of followers, and number of followees. We provide several benchmarks validating the positions of these entities and discuss several applications for this dataset.

Konrad Löhr, Shuzhou Yuan, Michael Färber

Large Language Models (LLMs) are increasingly integral to information dissemination and decision-making processes. Given their growing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propagation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to assess the inherent political leanings of these models. Subsequently, persona prompting with the PCT is used to explore explicit stereotypes across various social dimensions. In a final step, implicit stereotypes are uncovered by evaluating models with multilingual versions of the PCT. Key findings reveal a consistent left-leaning political alignment across all investigated models. Furthermore, while the nature and extent of stereotypes vary considerably between models, implicit stereotypes elicited through language variation are more pronounced than those identified via explicit persona prompting. Interestingly, for most models, implicit and explicit stereotypes show a notable alignment, suggesting a degree of transparency or "awareness" regarding their inherent biases. This study underscores the complex interplay of political bias and stereotypes in LLMs.

Tabia Tanzin Prama, Chhandak Bagchi, Vishal Kalakonnavar et al.

This study examines whether German X users would see politically balanced news feeds if they followed comparable leading politicians from each federal parliamentary party of Germany. We address this question using an algorithmic audit tool [1] and all publicly available posts published by 436 German politicians on X. We find that the default feed of X showed more content from far-right AfD than from other political parties. We analyze potential factors influencing feed content and the resulting political non-representativeness of X. Our findings suggest that engagement measures and unknown factors related to party affiliation contribute to the overrepresentation of extremes of the German political party spectrum in the default algorithmic feed of X.

Charlott Jakob, David Harbecke, Patrick Parschan et al.

Large Language Models are increasingly used in applications requiring objective assessment, which could be compromised by political bias. Many studies found preferences for left-leaning positions in LLMs, but downstream effects on tasks like fact-checking remain underexplored. In this study, we systematically investigate political bias through exchanging words with euphemisms or dysphemisms in German claims. We construct minimal pairs of factually equivalent claims that differ in political connotation, to assess the consistency of LLMs in classifying them as true or false. We evaluate six LLMs and find that, more than political leaning, the presence of judgmental words significantly influences truthfulness assessment. While a few models show tendencies of political bias, this is not mitigated by explicitly calling for objectivism in prompts. Warning: This paper contains content that may be offensive or upsetting.

Kirill Chmel, Eunji Kim, John Marshall et al.

Political apathy and skepticism of traditional authorities are increasingly common, but social media creators (SMCs) capture the public's attention. Yet whether these seemingly-frivolous actors shape political attitudes and behaviors remains largely unknown. Our pre-registered field experiment encouraged Americans aged 18-45 to start following five progressive-minded SMCs on Instagram, TikTok, or YouTube between August and December 2024. We varied recommendations to follow SMCs producing predominantly-political (PP), predominantly-apolitical (PA), or entirely non-political (NP) content, and cross-randomized financial incentives to follow assigned SMCs. Beyond markedly increasing consumption of assigned SMCs' content, biweekly quiz-based incentives increased overall social media use by 10% and made participants more politically knowledgeable. These incentives to follow PP or PA SMCs led participants to adopt more liberal policy positions and grand narratives around election time, while PP SMCs more strongly shaped partisan evaluations and vote choice. PA SMCs were seen as more informative and trustworthy, generating larger effects per video concerning politics. Participants assigned to follow NP SMCs instead became more conservative, consistent with left-leaning participants using social media more when right-leaning content was ascendant. These effects exceed the impacts of traditional campaign outreach and partisan media, demonstrating the importance of SMCs as opinion leaders in the attention economy as well as trust- and volume-based mechanisms of political persuasion.

Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele et al.

Stance detection is an important task for many applications that analyse or support online political discussions. Common approaches include fine-tuning transformer based models. However, these models require a large amount of labelled data, which might not be available. In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model. Second, we propose a new active learning method called SQBC based on the "Query-by-Comittee" approach. The key idea is to use LLM-generated synthetic data as an oracle to identify the most informative unlabelled samples, that are selected for manual labelling. Comprehensive experiments show that both ideas can improve the stance detection performance. Curiously, we observed that fine-tuning on actively selected samples can exceed the performance of using the full dataset.

Morgan Wack, Kayla Duskin, Damian Hodel

Fact-checking has been promoted as a key method for combating political misinformation. Comparing the spread of election-related misinformation narratives along with their relevant political fact-checks, this study provides the most comprehensive assessment to date of the real-world limitations faced by political fact-checking efforts. To examine barriers to impact, this study extends recent work from laboratory and experimental settings to the wider online information ecosystem present during the 2022 U.S. midterm elections. From analyses conducted within this context, we find that fact-checks as currently developed and distributed are severely inhibited in election contexts by constraints on their i. coverage, ii. speed, and, iii. reach. Specifically, we provide evidence that fewer than half of all prominent election-related misinformation narratives were fact-checked. Within the subset of fact-checked claims, we find that the median fact-check was released a full four days after the initial appearance of a narrative. Using network analysis to estimate user partisanship and dynamics of information spread, we additionally find evidence that fact-checks make up less than 1.2\% of narrative conversations and that even when shared, fact-checks are nearly always shared within,rather than between, partisan communities. Furthermore, we provide empirical evidence which runs contrary to the assumption that misinformation moderation is politically biased against the political right. In full, through this assessment of the real-world influence of political fact-checking efforts, our findings underscore how limitations in coverage, speed, and reach necessitate further examination of the potential use of fact-checks as the primary method for combating the spread of political misinformation.

Priyanshu Priya, Mauajama Firdaus, Asif Ekbal

Computational approach to politeness is the task of automatically predicting and generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational politeness in natural language processing. We view four milestones in the research so far, viz. supervised and weakly-supervised feature extraction to identify and induce politeness in a given text, incorporation of context beyond the target text, study of politeness across different social factors, and study the relationship between politeness and various sociolinguistic cues. In this article, we describe the datasets, approaches, trends, and issues in computational politeness research. We also discuss representative performance values and provide pointers to future works, as given in the prior works. In terms of resources to understand the state-of-the-art, this survey presents several valuable illustrations, most prominently, a table summarizing the past papers along different dimensions, such as the types of features, annotation techniques, and datasets used.

Urs Zaberer, Sebastian Padó, Gabriella Lapesa

The identification and classification of political claims is an important step in the analysis of political newspaper reports; however, resources for this task are few and far between. This paper explores different strategies for the cross-lingual projection of political claims analysis. We conduct experiments on a German dataset, DebateNet2.0, covering the policy debate sparked by the 2015 refugee crisis. Our evaluation involves two tasks (claim identification and categorization), three languages (German, English, and French) and two methods (machine translation -- the best method in our experiments -- and multilingual embeddings).

Joana D’Arc Ferreira da Silva, Pedro Igor Guimarães Santos Xavier, Simone Ferreira de Assis et al.

O presente artigo apresenta uma proposta de pesquisa ainda em fase de implementação. O objetivo desse estudo é compreender a função da escola pública como um dispositivo essencial de apoio à rede de atenção à criança e adolescente vítima de violência. Em uma abordagem qualitativa de análise, esta pesquisa se baseia na pedagogia de Paulo Freire, entrelaçada à saúde coletiva na perspectiva da territorialidade. Para isso, recorremos à investigação exploratória aplicada de forma dedutiva, como coleta de dados e contextualização da temática, por meio da literatura disponível em plataformas online. A discussão e os resultados são frutos de uma análise crítica, baseada no conceito Freiriano de ação-reflexão-ação. Acreditamos que a educação em saúde, junto às instituições de ensino público, proporcionará, às crianças e adolescentes, um ambiente de troca de afetos e saberes, bem como proteção e prevenção delas próprias em suas comunidades.

Renan Santos Furtado

O presente estudo apresenta algumas contribuições teórico-conceituais de Paulo Freire para pensar a legitimidade da Educação Física escolar como componente da formação humana ampliada de crianças, jovens, adultos e idosos que frequentam a escola. Propõe-se analisar a problemática da legitimidade da Educação Física na escola, a partir do aporte teórico-conceitual freireano.  O trabalho tem como questão suleadora analisar as contribuições que esse aporte apresenta para o desenvolvimento de práticas pedagógicas emancipatórias na Educação Física escolar. Metodologicamente, trata-se de um ensaio teórico de orientação reflexiva, pautado em estudos de Paulo Freire e do campo da Educação Física brasileira. Em termos de apontamentos, o escrito sugere que é possível pensar as colaborações desse educador, ao mesmo tempo, os desafios do tempo presente para a Educação Física escolar nas dimensões ontológicas, epistemológicas e ético-políticas.

Natália Pereira Marinelli, Nayra da Costa e Silva Rego, Khelyane Mesquita de Carvalho et al.

O objetivo deste texto é relatar a experiência de educação em saúde realizada por tele acompanhamento a pacientes com suspeita de Covid-19 na Atenção Primária à Saúde, realizado na Unidade Básica de Saúde Novo Horizonte, em Teresina, Piauí, entre julho e dezembro de 2019. Durante o processo, foram acompanhados 504 pacientes, com cumprimento de requisitos para casos suspeitos de Covid-19 e seus respectivos contatos por meio da valorização da teleducação em saúde como um instrumento viável no combate à pandemia. Entretanto, essa prática apresenta grandes desafios, que serão discutidos neste texto a fim de constatar que o processo de educação em saúde, seja presencial ou à distância, é uma ferramenta que viabiliza a promoção e a prevenção da saúde, e que a Enfermagem exerce um papel fundamental nesse novo modo de trabalho surgido em contexto de pandemia.

Laura Manuela Rezende Silveira, Helena Mariotto Palma, Maria Thereza Ramos Souza et al.

O objetivo deste relato é apresentar a experiência vivenciada por quatro discentes do curso de fisioterapia no transcorrer da disciplina Fisioterapia no Trabalho, a qual desenvolve ações voltadas à saúde do trabalhador. Aborda aspectos históricos do trabalho e destaca a influência da pandemia de COVID-19 decretada no início de 2020 no contexto do trabalho. A ênfase é dada ao trabalho no formato home office, modalidade adotada para contribuir com as medidas sanitárias impostas. Considerando as reais possibilidades deste formato de trabalho perdurar, nossas ações foram direcionadas para os pontos negativos desencadeados pelo home office, dentre eles a dificuldade em estabelecer limites entre o ambiente profissional e o pessoal, a necessidade de alterar a infraestrutura da casa, a falta de socialização e possíveis conflitos familiares. Deste modo, entendemos que a saúde física e emocional do trabalhador é afetada. A fim de, contribuir com a proteção e promoção da saúde do trabalhador, elaboramos uma cartilha com exercícios específicos àqueles em home office. Ao finalizarmos a cartilha e a disponibilizá-la entendemos a importância da disciplina para a formação do profissional fisioterapeuta e o quanto podemos fazer a diferença na saúde do trabalhador.

Jaehyuk Choi, Lei Lu, Heungju Park et al.

This paper examines the effect of the political network of Chinese municipal leaders on the pricing of municipal corporate bonds. Using municipal leaders' working experience to measure the political network, we find that this network reduces the bond issuance yield spreads by improving the credit ratings of the issuer, the local government financing vehicle. The relationship between political networks and issuance yield spreads is strengthened in areas where financial markets and legal systems are less developed.

Shangbin Feng, Zhaoxuan Tan, Zilong Chen et al.

Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic ideological analysis. In this paper, we propose \textbf{PAR}, a \textbf{P}olitical \textbf{A}ctor \textbf{R}epresentation learning framework that jointly leverages social context and expert knowledge. Specifically, we retrieve and extract factual statements about legislators to leverage social context information. We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations. Finally, we train PAR with three objectives to align representation learning with expert knowledge, model ideological stance consistency, and simulate the echo chamber phenomenon. Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction. Further analysis proves that PAR learns representations that reflect the political reality and provide new insights into political behavior.

Hernán Camarero

Presentación

AAVV

Número completo

Ruibo Liu, Chenyan Jia, Jason Wei et al.

Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.

Ying Sheng, Yoni Zohar, Christophe Ringeissen et al.

We make two contributions to the study of polite combination in satisfiability modulo theories. The first contribution is a separation between politeness and strong politeness, by presenting a polite theory that is not strongly polite. This result shows that proving strong politeness (which is often harder than proving politeness) is sometimes needed in order to use polite combination. The second contribution is an optimization to the polite combination method, obtained by borrowing from the Nelson-Oppen method. In its non-deterministic form, the Nelson-Oppen method is based on guessing arrangements over shared variables. In contrast, polite combination requires an arrangement over \emph{all} variables of the shared sort (not just the shared variables). We show that when using polite combination, if the other theory is stably infinite with respect to a shared sort, only the shared variables of that sort need be considered in arrangements, as in the Nelson-Oppen method. Reasoning about arrangements of variables is exponential in the worst case, so reducing the number of variables that are considered has the potential to improve performance significantly. We show preliminary evidence for this in practice by demonstrating a speed-up on a smart contract verification benchmark.