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

Thilo Hagendorff

The guiding principle of AI alignment is to train large language models (LLMs) to be harmless, helpful, and honest (HHH). At the same time, there are mounting concerns that LLMs exhibit a left-wing political bias. Yet, the commitment to AI alignment cannot be harmonized with the latter critique. In this article, I argue that intelligent systems that are trained to be harmless and honest must necessarily exhibit left-wing political bias. Normative assumptions underlying alignment objectives inherently concur with progressive moral frameworks and left-wing principles, emphasizing harm avoidance, inclusivity, fairness, and empirical truthfulness. Conversely, right-wing ideologies often conflict with alignment guidelines. Yet, research on political bias in LLMs is consistently framing its insights about left-leaning tendencies as a risk, as problematic, or concerning. This way, researchers are actively arguing against AI alignment, tacitly fostering the violation of HHH principles.

Caroline Violot, Vera Sosnovik, Mathias Humbert

In 2024, France was shaken by the far-right National Rally's victory in the European elections. In response to this unprecedented result, French President Emmanuel Macron dissolved the National Assembly, triggering legislative elections just two weeks later. A whirlwind campaign followed, partly on social media, as is now the norm, and concluded with the victory of a left-wing coalition. This article examines the YouTube activity of two key actors during this period, news media and politicians, and the commenting behavior they generated. We built a dataset of 35 news media channels, 28 politicians and parties channels, 43.5k videos posted from three months before the European elections to one week after the second round of the legislative elections, and 7.4M associated comments. We examined upload activity and engagement across political orientations and used network analysis methods to uncover the structure of their commenting communities. We also identified politicians' appearances on news media channels and assessed their impact on commenting user bases. Our findings show that, among politicians and parties channels, far-right and left-wing ones were significantly more active and received substantially higher engagement (views, likes, and comments) than other groups, with denser and more clustered commenting communities. About 7% of commenters commented across political orientations and were much more active than in-group commenters. News media channels tended to favor politically aligned guests, while centrist politicians were over-represented. Finally, politicians' presence in the videos of a specific news media channel increased the share of commenters who were active on this channel and political channels, regardless of their orientation.

Jinrui Yang, Xudong Han, Timothy Baldwin

We introduce EuroParlVote, a novel benchmark for evaluating large language models (LLMs) in politically sensitive contexts. It links European Parliament debate speeches to roll-call vote outcomes and includes rich demographic metadata for each Member of the European Parliament (MEP), such as gender, age, country, and political group. Using EuroParlVote, we evaluate state-of-the-art LLMs on two tasks -- gender classification and vote prediction -- revealing consistent patterns of bias. We find that LLMs frequently misclassify female MEPs as male and demonstrate reduced accuracy when simulating votes for female speakers. Politically, LLMs tend to favor centrist groups while underperforming on both far-left and far-right ones. Proprietary models like GPT-4o outperform open-weight alternatives in terms of both robustness and fairness. We release the EuroParlVote dataset, code, and demo to support future research on fairness and accountability in NLP within political contexts.

Sian Lee, Haeseung Seo, Aiping Xiong et al.

Political misinformation, particularly harmful when it aligns with individuals' preexisting beliefs and political ideologies, has become widespread on social media platforms. In response, platforms like Facebook and X introduced warning messages leveraging fact-checking results from third-party fact-checkers to alert users against false content. However, concerns persist about the effectiveness of these fact-checks, especially when fact-checkers are perceived as politically biased. To address these concerns, this study presents findings from an online human-subject experiment (N=216) investigating how the political stances of fact-checkers influence their effectiveness in correcting misbeliefs about political misinformation. Our findings demonstrate that partisan fact-checkers can decrease the perceived accuracy of political misinformation and correct misbeliefs without triggering backfire effects. This correction is even more pronounced when the misinformation aligns with individuals' political ideologies. Notably, while previous research suggests that fact-checking warnings are less effective for conservatives than liberals, our results suggest that explicitly labeled partisan fact-checkers, positioned as political counterparts to conservatives, are particularly effective in reducing conservatives' misbeliefs toward pro-liberal misinformation.

Mario Villagran

The choice of protest tactics in a social movement has often been analyzed based on the demands, participants, and internal characteristics of the movement. However, recent evidence highlights the context or setting in which the demonstration takes place as another key element in the process; Using structural equation modeling, studies have shown a link between high perceptions of injustice in the treatment received by authorities and a greater acceptance of non-normative and/or violent methods of protest. In line with this approach, this article aims to examine the extent to which another form of authority legitimacy -- such as political trust -- affects the overall justification for the use of violence by both protesters and the police. Using longitudinal data from Chile (2016 -- 2019), which captures the collective protests of the ``Social Outbreak'', three analytical approaches -- fixed effects, cross-lagged, and multilevel models -- demonstrate that declining political trust not only weakened public acceptance of police violence but also increased tolerance toward protesters' use of violent tactics. This relationship adds a new dimension to the analysis of violent protests, suggesting that low political trust in many modern states may be a contributing factor to the increasing radicalization of demonstrations in recent years.

Benjamin Przybocki, Guilherme V. Toledo, Yoni Zohar

Shininess and strong politeness are properties related to theory combination procedures. In a paper titled "Many-sorted equivalence of shiny and strongly polite theories", Casal and Rasga proved that for decidable theories, these properties are equivalent. We refine their result by showing that: (i) shiny theories are always decidable, and therefore strongly polite; and (ii) there are (undecidable) strongly polite theories that are not shiny. This line of research is tightly related to a recent series of papers that have sought to classify all the relations between theory combination properties. We finally complete this project, resolving all of the remaining problems that were previously left open.

Vivek Sharma, Mohammad Mahdi Shokri, Sarah Ita Levitan et al.

News outlets are well known to have political associations, and many national outlets cultivate political biases to cater to different audiences. Journalists working for these news outlets have a big impact on the stories they cover. In this work, we present a methodology to analyze the role of journalists, affiliated with popular news outlets, in propagating their bias using some form of propaganda-like language. We introduce JMBX(Journalist Media Bias on X), a systematically collected and annotated dataset of 1874 tweets from Twitter (now known as X). These tweets are authored by popular journalists from 10 news outlets whose political biases range from extreme left to extreme right. We extract several insights from the data and conclude that journalists who are affiliated with outlets with extreme biases are more likely to use propaganda-like language in their writings compared to those who are affiliated with outlets with mild political leans. We compare eight different Large Language Models (LLM) by OpenAI and Google. We find that LLMs generally performs better when detecting propaganda in social media and news article compared to BERT-based model which is fine-tuned for propaganda detection. While the performance improvements of using large language models (LLMs) are significant, they come at a notable monetary and environmental cost. This study provides an analysis of both the financial costs, based on token usage, and the environmental impact, utilizing tools that estimate carbon emissions associated with LLM operations.

Stanley Cao, Felix Drinkall

Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the optimal way to incorporate metadata into a political stance detection task. We demonstrate that previous methods combining metadata with language-based data for political stance detection have not fully utilized the metadata information; our simple baseline, using only party membership information, surpasses the current state-of-the-art. We then show that prepending metadata (e.g., party and policy) to political speeches performs best, outperforming all baselines, indicating that complex metadata inclusion systems may not learn the task optimally.

Yujin Potter, Shiyang Lai, Junsol Kim et al.

How could LLMs influence our democracy? We investigate LLMs' political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. presidential election context. Through a voting simulation, we first demonstrate 18 open- and closed-weight LLMs' political preference for a Democratic nominee over a Republican nominee. We show how this leaning towards the Democratic nominee becomes more pronounced in instruction-tuned models compared to their base versions by analyzing their responses to candidate-policy related questions. We further explore the potential impact of LLMs on voter choice by conducting an experiment with 935 U.S. registered voters. During the experiments, participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. The experiment results show a shift in voter choices towards the Democratic nominee following LLM interaction, widening the voting margin from 0.7% to 4.6%, even though LLMs were not asked to persuade users to support the Democratic nominee during the discourse. This effect is larger than many previous studies on the persuasiveness of political campaigns, which have shown minimal effects in presidential elections. Many users also expressed a desire for further political interaction with LLMs. Which aspects of LLM interactions drove these shifts in voter choice requires further study. Lastly, we explore how a safety method can make LLMs more politically neutral, while raising the question of whether such neutrality is truly the path forward.

Samiran Gode, Supreeth Bare, Bhiksha Raj et al.

Our paper aims to analyze political polarization in US political system using Language Models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates views on the economy, healthcare, education and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a Language model based method that helps analyze how polarized a candidate is. Our data is divided into 2 parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based on reason and be independent of factors such as birthplace, alma mater, etc. We further split this data into 4 phases chronologically, to help understand if and how the polarization amongst candidates changes. This data has been cleaned to remove biases. To understand the polarization we begin by showing results from some classical language models in Word2Vec and Doc2Vec. And then use more powerful techniques like the Longformer, a transformer based encoder, to assimilate more information and find the nearest neighbors of each candidate based on their political view and their background.

Jukka Ruohonen

Data protection laws and policies have been studied extensively in recent years, but little is known about the parliamentary politics of data protection. This imitation applies even to the European Union (EU) that has taken the global lead in data protection and privacy regulation. For patching this notable gap in existing research, this paper explores the data protection questions raised by the Members of the European Parliament (MEPs) in the Parliament's plenary sessions and the answers given to these by the European Commission. Over a thousand of such questions and answers are covered in a period from 1995 to early 2023. Given computational analysis based on text mining, the results indicate that (a) data protection has been actively debated in the Parliament during the past twenty years. No noticeable longitudinal trends are present; the debates have been relatively constant. As could be expected, (b) the specific data protection laws in the EU have frequently been referenced in these debates, which (c) do not seem to align along conventional political dimensions such as the left-right axis. Furthermore, (d) numerous distinct data protection topics have been debated by the parliamentarians, indicating that data protection politics in the EU go well-beyond the recently enacted regulations.

Jonathan H. Rystrøm

As generative language models are deployed in ever-wider contexts, concerns about their political values have come to the forefront with critique from all parts of the political spectrum that the models are biased and lack neutrality. However, the question of what neutrality is and whether it is desirable remains underexplored. In this paper, I examine neutrality through an audit of Delphi [arXiv:2110.07574], a large language model designed for crowdsourced ethics. I analyse how Delphi responds to politically controversial questions compared to different US political subgroups. I find that Delphi is poorly calibrated with respect to confidence and exhibits a significant political skew. Based on these results, I examine the question of neutrality from a data-feminist lens, in terms of how notions of neutrality shift power and further marginalise unheard voices. These findings can hopefully contribute to a more reflexive debate about the normative questions of alignment and what role we want generative models to play in society.

Danendra Athallariq Harya Putra, Arief Purnama Muharram

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling; TensorFlow for deep learning handling; and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard.

Sadia Kamal, Brenner Little, Jade Gullic et al.

Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. The common research practice typically examines the biased structure of online user communities for a given topic or qualitatively measuring the impacts of polarized topics on social media. However, there is limited work focusing on analyzing polarization at the ground-level, specifically in the social media posts themselves. Such existing analysis heavily relies on annotated data, which often requires laborious human labeling, offers labels only to specific problems, and lacks the ability to determine the near-future bias state of a social media conversations. Understanding the degree of political orientation conveyed in social media posts is crucial for quantifying the bias of online user communities and investigating the spread of polarized content. In this work, we first introduce two heuristic methods that leverage on news media bias and post content to label social media posts. Next, we compare the efficacy and quality of heuristically labeled dataset with a randomly sampled human-annotated dataset. Additionally, we demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts, employing both traditional supervised learning and few-shot learning setups. We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.

Daniel R. Trejo

A 60 años de haber triunfado, la Revolución cubana sigue suscitando apasionados debates sobre su significación y realidades, pues despertó la esperanza de un cambio profundo en los sinos de los pueblos americanos. Sin embargo, una familia de izquierda la cuestionó en sus orígenes mismos, advirtiendo muy temprano el peligroso giro dado hacia el socialismo de estilo soviético. En esa tesitura este artículo examina cómo reaccionaron y qué lecturas elaboraron los anarquistas ante ese proceso. El análisis se realizó a partir de diferentes publicaciones y documentos producidos por organizaciones libertarias de Argentina, Cuba, México y Uruguay entre 1960 y 1962.

Edileuza Ricardo da Silva, Gabriel Nóbrega Vieira, Pedro José Santos Carneiro Cruz et al.

O presente artigo apresenta resultados de pesquisa cujo objeto central rescindiu na análise de uma experiência com práticas educativas em grupos comunitários na Atenção Básica (AB), pautada pela perspectiva pedagógica freiriana da educação popular (EP). Seu processo investigativo teve, como fio condutor, o desvelamento de desafios nesse contexto, tendo também, como objeto, a experiência do Programa de Extensão “PINAB – Práticas Integrais de Promoção da Saúde e Nutrição na Atenção Básica”, vinculado ao Departamento de Nutrição/CCS e ao Departamento de Promoção da Saúde/CCM da Universidade Federal da Paraíba. O caminho investigativo foi construído por meio de uma pesquisa qualitativa feita com a realização de entrevistas, com abordagem oral e individual. Foram valorizadas percepções de diferentes sujeitos das comunidades em que atua o referido programa de extensão, como de trabalhadores da Unidade de Saúde da Família Vila Saúde, todos efetivos participantes das práticas educativas analisadas criticamente nesta pesquisa. Como potencialidade, viu-se que o PINAB atua na AB com Grupos Operativos, frentes essas que servem de alicerce para o Programa, buscando a construção de uma visão humanizada da saúde com o olhar voltado para a integralidade do indivíduo, bem como associada à realidade social das comunidades, trabalhando de forma interdisciplinar e buscando compreender a complexidade do sujeito em uma atividade integral.

Pedro Forquesato

A main issue in improving public sector efficiency is to understand to what extent public appointments are based on worker capability, instead of being used to reward political supporters (patronage). I contribute to a recent literature documenting patronage in public sector employment by establishing what type of workers benefit the most from political connections. Under the (empirically supported) assumption that in close elections the result of the election is as good as random, I estimate a causal forest to identify heterogeneity in the conditional average treatment effect of being affiliated to the party of the winning mayor. Contrary to previous literature, for most positions we find positive selection on education, but a negative selection on (estimated) ability. Overall, unemployed workers or low tenure employees that are newly affiliated to the winning candidate's party benefit the most from political connections, suggesting that those are used for patronage.

Hanjia Lyu, Jiebo Luo

Understanding political polarization on social platforms is important as public opinions may become increasingly extreme when they are circulated in homogeneous communities, thus potentially causing damage in the real world. Automatically detecting the political ideology of social media users can help better understand political polarization. However, it is challenging due to the scarcity of ideology labels, complexity of multimodal contents, and cost of time-consuming data collection process. In this study, we adopt a heterogeneous graph neural network to jointly model user characteristics, multimodal post contents as well as user-item relations in a bipartite graph to learn a comprehensive and effective user embedding without requiring ideology labels. We apply our framework to online discussions about economy and public health topics. The learned embeddings are then used to detect political ideology and understand political polarization. Our framework outperforms the unimodal, early/late fusion baselines, and homogeneous GNN frameworks by a margin of at least 9% absolute gain in the area under the receiver operating characteristic on two social media datasets. More importantly, our work does not require a time-consuming data collection process, which allows faster detection and in turn allows the policy makers to conduct analysis and design policies in time to respond to crises. We also show that our framework learns meaningful user embeddings and can help better understand political polarization. Notable differences in user descriptions, topics, images, and levels of retweet/quote activities are observed. Our framework for decoding user-content interaction shows wide applicability in understanding political polarization. Furthermore, it can be extended to user-item bipartite information networks for other applications such as content and product recommendation.

Thelma Maria Grisi Velôso, Karolina Mirella Oliveira Pereira Costa, Leonardo Farias de Arruda et al.

Neste texto, relata-se uma experiência de extensão universitária na área da Psicologia Social Comunitária, realizada, no meio rural, com crianças e adolescentes de um assentamento constituído pelo Movimento dos Trabalhadores Rurais sem Terra (MST) (Campina Grande/PB). Aliou-se à proposta da Educação Popular a utilização de linguagens artísticas. As oficinas psicopedagógicas realizadas com as crianças e os adolescentes assentados tiveram como objetivo estimular o gosto pela leitura, a reflexão crítica e o protagonismo social. Recorreu-se às diversas linguagens artísticas, entre elas, o método do Teatro do Oprimido proposto por Augusto Boal. Neste trabalho, são relatadas as oficinas realizadas com esse grupo que culminaram com a apresentação de uma encenação teatral para a comunidade. As linguagens artísticas foram um recurso potente para colocar em prática a proposta de acordo com as premissas da Educação Popular.

Mayara Sousa Ferreira, Ruthy Manuella de Brito Costa

A formação acadêmica tem como base o ensino, a pesquisa e a extensão, que precisam ocorrer de maneira coordenada, visando uma formação que contemple teoria, prática, bem como investigação científica. No curso de Jornalismo, atividades de extensão oportunizam aos estudantes práticas que se aproximam daquelas que se dão na ambiência profissional. Sendo assim, o presente trabalho tem como objetivo descrever a experiência do projeto de extensão do referido curso na Faculdade R. Sá, a partir da perspectiva da produção de notícias, pesquisa documental e produção fotográfica, através da revista Das Antigas. Com uma abordagem qualitativa, a construção deste trabalho se deu com base em pesquisa documental e de campo. Teoricamente, o trabalho se ampara principalmente em Duarte (2012), Goulart (2004) e Sousa (2004). Os resultados evidenciam a importância e a necessidade do aprimoramento das práticas extensionistas interdisciplinares para formação do jornalista. Além disso, é uma forma de relacionar ambiente acadêmico com a sociedade.