David Elsweiler, Christine Elsweiler, Anna Ziegner
Politeness is a core dimension of human communication, yet its role in human-AI information seeking remains underexplored. We investigate how user politeness behaviour shapes conversational outcomes in a cooking-assistance setting. First, we annotated 30 dialogues, identifying four distinct user clusters ranging from Hyperpolite to Hyperefficient. We then scaled up to 18,000 simulated conversations across five politeness profiles (including impolite) and three open-weight models. Results show that politeness is not only cosmetic: it systematically affects response length, informational gain, and efficiency. Engagement-seeking prompts produced up to 90% longer replies and 38% more information nuggets than hyper-efficient prompts, but at markedly lower density. Impolite inputs yielded verbose but less efficient answers, with up to 48% fewer nuggets per watt-hour compared to polite input. These findings highlight politeness as both a fairness and sustainability issue: conversational styles can advantage or disadvantage users, and "polite" requests may carry hidden energy costs. We discuss implications for inclusive and resource-aware design of information agents.
Research on the causes of political polarization points towards multiple drivers of the problem, from social and psychological to economic and technological. However, political institutions stand out, because -- while capable of exacerbating or alleviating polarization -- they can be re-engineered more readily than others. Accordingly, we analyze one class of such institutions -- electoral systems -- investigating whether the large-party seat bias found in many common systems (particularly plurality and Jefferson-D'Hondt) exacerbates polarization. Cross-national empirical data being relatively sparse and heavily confounded, we use computational methods: an agent-based Monte Carlo simulation. We model voter behavior over multiple electoral cycles, building upon the classic spatial model, but incorporating other known voter behavior patterns, such as the bandwagon effect, strategic voting, preference updating, retrospective voting, and the thermostatic effect. We confirm our hypothesis that electoral systems with a stronger large-party bias exhibit significantly higher polarization, as measured by the Mehlhaff index.
A los 25 años, en 1923, el argentino Félix Weil fundó el Institut für Sozialforschung con una donación de su padre, el comerciante de cereales Hermann Weil. Weil fue alumno de Robert Wilbrandt, profesor de la Universidad de Tubinga, que formó parte de la Comisión de Socialización tras la revolución alemana en 1918-1919. Wilbrandt animó a Weil a convertir un ensayo suyo sobre la socialización en una disertación, que finalmente se publicó en 1921. El texto de Weil, Socialización, nunca se ha traducido, y casi nunca se ha comentado. En este artículo, hablaré del texto, mostrando el contexto en el que surgió: el debate sobre la socialización entre 1918 y 1921 en Alemania.
1789-, Labor in politics. Political activity of the working class
El artículo toma como hilo la participación de Félix Weil, durante la década del 30, en el Colegio Libre de Estudios Superiores y su revista Cursos y Conferencias. Analiza las discusiones propuestas por los tres cursos que dictó Weil a partir de su vinculación con el Partido Socialista Independiente y el Ministerio de Hacienda, con la circulación del materialismo dialéctico y con la construcción de un frente cultural antifascista. Ello permite rescatar una primera recepción argentina del Instituto de Investigación Social en Frankfurt anclada en la economía marxista.
1789-, Labor in politics. Political activity of the working class
We present a critical discourse analysis of the 2024 U.S. presidential debates, examining Donald Trump's rhetorical strategies in his interactions with Joe Biden and Kamala Harris. We introduce a novel annotation framework, BEADS (Bias Enriched Annotation for Dialogue Structure), which systematically extends the DAMSL framework to capture bias driven and adversarial discourse features in political communication. BEADS includes a domain and language agnostic set of tags that model ideological framing, emotional appeals, and confrontational tactics. Our methodology compares detailed human annotation with zero shot ChatGPT assisted tagging on verified transcripts from the Trump and Biden (19,219 words) and Trump and Harris (18,123 words) debates. Our analysis shows that Trump consistently dominated in key categories: Challenge and Adversarial Exchanges, Selective Emphasis, Appeal to Fear, Political Bias, and Perceived Dismissiveness. These findings underscore his use of emotionally charged and adversarial rhetoric to control the narrative and influence audience perception. In this work, we establish BEADS as a scalable and reproducible framework for critical discourse analysis across languages, domains, and political contexts.
Daniele Cirulli, Antonio Desiderio, Giulio Cimini
et al.
Political debate nowadays takes place mainly on online social media, with election periods amplifying ideological engagement. Reddit is generally considered more resistant to polarization and echo chamber effects than platforms like Twitter or Facebook. Here, we challenge this assumption through a case study across the 2016 US presidential election. We use statistical validation techniques to extract ideologically distinct communities of subreddits, in terms of their contributing user base and news consumption, which we use to analyze the dynamics of political debate. We thus reveal clear polarization in both interaction-based and topic-based communities, with clusters of Democratic, Conservative, and Banned subreddits. Election periods intensify cross-group engagement, align Banned and Conservative content, and reduce linguistic diversity within groups. Overall we characterize Reddit as a polarized environment marked by the presence of echo chambers, highlighting network validation as a key method for identifying behavioral and interaction patterns on online social media.
O presente artigo tem por objetivo discutir acerca dos saberes e fazeres matemáticos realizados por pedreiros do município de Amapá, no estado do Amapá, com vistas a reforçar que a matemática aplicada tem uma grande eficácia na construção do conhecimento empírico. O trabalho objetiva ainda descrever como esses saberes e fazeres matemáticos são transmitidos e repassados por essas famílias. A pesquisa tem caráter qualitativo, exploratório e descritivo. Para tanto, foi realizada observação direta por meio de visita in loco em obras realizadas no município. Os dados da pesquisa foram obtidos a partir de conversas informais com um pequeno grupo de oito pedreiros, cujos recortes de inclusão e exclusão foram os pedreiros mais antigos do município e que atuam na área há mais tempo. Os resultados apontam para um rico conjunto de conhecimentos matemáticos (empíricos e tradicionais) que vêm sendo adquiridos e transmitidos ao longo dos anos. Além disso, os dados levam à reflexão quanto à prática pedagógica do ensino e da aprendizagem da matemática, assim como quanto à importância do estudo da cultura e a aplicabilidade desta em sala de aula como forma de valorizar e manter as tradições locais e os conhecimentos matemáticos.
Social Sciences, Labor in politics. Political activity of the working class
Philipe Melo, João M. M. Couto, Daniel Kansaon
et al.
With the increasing use of smartphones, instant messaging platforms turned into important communication tools. According to WhatsApp, more than 100 billion messages are sent each day on the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and stickers. Particularly, stickers are a new multimedia format that emerged with messaging apps, promoting new forms of interactions among users, especially in the Brazilian context, transcending their role as a mere form of humor to become a key element in political strategy. In this regard, we investigate how stickers are being used, unveiling unique characteristics that these media bring to WhatsApp chats and the political use of this new media format. To achieve that, we collected a large sample of messages from WhatsApp public political discussion groups in Brazil and analyzed the sticker messages shared in this context
Sergei Bazylik, Magne Mogstad, Joseph Romano
et al.
It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather than the true share of support, there may be considerable uncertainty concerning the true ranking of the political candidates or parties. In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each category. We consider both the problem of constructing marginal confidence sets for the rank of a particular category as well as simultaneous confidence sets for the ranks of all categories. A distinguishing feature of our analysis is that we exploit the multinomial structure of the data to develop confidence sets that are valid in finite samples. We additionally develop confidence sets using the bootstrap that are valid only approximately in large samples. We use our methodology to rank political parties in Australia using data from the 2019 Australian Election Survey. We find that our finite-sample confidence sets are informative across the entire ranking of political parties, even in Australian territories with few survey respondents and/or with parties that are chosen by only a small share of the survey respondents. In contrast, the bootstrap-based confidence sets may sometimes be considerably less informative. These findings motivate us to compare these methods in an empirically-driven simulation study, in which we conclude that our finite-sample confidence sets often perform better than their large-sample, bootstrap-based counterparts, especially in settings that resemble our empirical application.
Ernesto Colacrai, Federico Cinus, Gianmarco De Francisci Morales
et al.
The prevalent perspective in quantitative research on opinion dynamics flattens the landscape of the online political discourse into a traditional left--right dichotomy. While this approach helps simplify the analysis and modeling effort, it also neglects the intrinsic multidimensional richness of ideologies. In this study, we analyze social interactions on Reddit, under the lens of a multi-dimensional ideological framework: the political compass. We examine over 8 million comments posted on the subreddits /r/PoliticalCompass and /r/PoliticalCompassMemes during 2020--2022. By leveraging their self-declarations, we disentangle the ideological dimensions of users into economic (left--right) and social (libertarian--authoritarian) axes. In addition, we characterize users by their demographic attributes (age, gender, and affluence). We find significant homophily for interactions along the social axis of the political compass and demographic attributes. Compared to a null model, interactions among individuals of similar ideology surpass expectations by 6%. In contrast, we uncover a significant heterophily along the economic axis: left/right interactions exceed expectations by 10%. Furthermore, heterophilic interactions are characterized by a higher language toxicity than homophilic interactions, which hints at a conflictual discourse between every opposite ideology. Our results help reconcile apparent contradictions in recent literature, which found a superposition of homophilic and heterophilic interactions in online political discussions. By disentangling such interactions into the economic and social axes we pave the way for a deeper understanding of opinion dynamics on social media.
Mexico has experienced a notable surge in assassinations of political candidates and mayors. This article argues that these killings are largely driven by organized crime, aiming to influence candidate selection, control local governments for rent-seeking, and retaliate against government crackdowns. Using a new dataset of political assassinations in Mexico from 2000 to 2021 and instrumental variables, we address endogeneity concerns in the location and timing of government crackdowns. Our instruments include historical Chinese immigration patterns linked to opium cultivation in Mexico, local corn prices, and U.S. illicit drug prices. The findings reveal that candidates in municipalities near oil pipelines face an increased risk of assassination due to drug trafficking organizations expanding into oil theft, particularly during elections and fuel price hikes. Government arrests or killings of organized crime members trigger retaliatory violence, further endangering incumbent mayors. This political violence has a negligible impact on voter turnout, as it targets politicians rather than voters. However, voter turnout increases in areas where authorities disrupt drug smuggling, raising the chances of the local party being re-elected. These results offer new insights into how criminal groups attempt to capture local governments and the implications for democracy under criminal governance.
Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele
et al.
Stance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarization or to facilitate more balanced discussions. Typically, transformer-based models are employed directly for stance detection, requiring vast amounts of data. However, the wide variety of debate topics in online political discussions makes data collection particularly challenging. LLMs have revived stance detection, but their online deployment in online political discussions faces challenges like inconsistent outputs, biases, and vulnerability to adversarial attacks. We show how LLM-generated synthetic data can improve stance detection for online political discussions by using reliable traditional stance detection models for online deployment, while leveraging the text generation capabilities of LLMs for synthetic data generation in a secure offline environment. To achieve this, (i) we generate synthetic data for specific debate questions by prompting a Mistral-7B model and show that fine-tuning with the generated synthetic data can substantially improve the performance of stance detection, while remaining interpretable and aligned with real world data. (ii) Using the synthetic data as a reference, we can improve performance even further by identifying the most informative samples in an unlabelled dataset, i.e., those samples which the stance detection model is most uncertain about and can benefit from the most. By fine-tuning with both synthetic data and the most informative samples, we surpass the performance of the baseline model that is fine-tuned on all true labels, while labelling considerably less data.
AbstractFollowing the 2014–2015 oil price crisis, service companies in Kazakhstan went through a process of industrial restructuring centered on workforce reduction and a concomitant increase of labor outsourcing. Taking the restructuring – or “optimization” – of state-owned service companies in the region of Mangystau as a starting point, this paper illustrates the heterogenous precarization effects and forms of precarity catalyzed by the process. Taking a multidimensional approach, the paper describes and analyses the effects of precarization in both socio-economic and political terms, as well as the implications for the production of differentiated laboring subjectivities. It situates the ethnographic trajectories of workers within the framework of Kazakhstan's authoritarian neoliberalism, highlighting the punitive and pastoral techniques of goverment deployed in the restructuring of the regional oil complex. In the first part, the article describes how precarization was experienced by workers as “slavery”, entailing the loss of social recognition as well as the intensification of economic exploitation and political domination, heightening their exposure to social and bodily vulnerability. The second section looks instead at the workings of a governmental agency in its effort to remake redundant workers into small business owners through the acquisition of entrepreneurial skills and the abandonment of the Soviet “dependency mindset”. The third and last section of the article concentrates on the individual trajectory of a dismissed worker joining a multi-level marketing scheme in order to cleanse himself from the bodily and social toxicity of precarized work in the oil industry.
AbstractThe simultaneous processes of secular state-building and state-led industrialisation resulted in a new ideology of women's labor in Turkey in the 1930s and the first half of the 1940s. As the country moved away from protectionist, state-led industrialisation in the post-war period, female industrial labor received increasing and contradictory attention from policy makers, employers, the new trade union movement, and middle-class feminists. On the one hand, there emerged an idealized image of factory women that emphasized their productive potential by metaphorically linking them with technology and mass production. However, this proud, progressive message was counterbalanced by an anxious, conservative view of young women's work—one that criticized factory girls’ consumption choices as posing a threat to respectable femininity. Weaving together lines of inquiry such as the change in industrialisation policy, women's access to technology, the sexual division of labor, and the emergent consumption patterns, I unpack the tropes of working-class productivity and femininity against the backdrop of the post-war expansion of capitalism in Turkey.
Venezuela has suffered three economic catastrophes since independence: one each in the nineteenth, twentieth, and twenty-first centuries. Prominent explanations for this trilogy point to the interaction of class conflict and resource dependence. We turn attention to intra-class conflict, arguing that the most destructive policy choices stemmed not from the rich defending themselves against the masses but rather from pitched battles among elites. Others posit that Venezuelan political institutions failed to sustain growth because they were insufficiently inclusive; we suggest in addition that they inadequately mediated intra-elite conflict.
While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g., learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g., with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan "All US Adults," or partisan "Democrat" and "Republican") and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further.
Elizaveta Kuznetsova, Mykola Makhortykh, Victoria Vziatysheva
et al.
This article presents a comparative analysis of the ability of two large language model (LLM)-based chatbots, ChatGPT and Bing Chat, recently rebranded to Microsoft Copilot, to detect veracity of political information. We use AI auditing methodology to investigate how chatbots evaluate true, false, and borderline statements on five topics: COVID-19, Russian aggression against Ukraine, the Holocaust, climate change, and LGBTQ+ related debates. We compare how the chatbots perform in high- and low-resource languages by using prompts in English, Russian, and Ukrainian. Furthermore, we explore the ability of chatbots to evaluate statements according to political communication concepts of disinformation, misinformation, and conspiracy theory, using definition-oriented prompts. We also systematically test how such evaluations are influenced by source bias which we model by attributing specific claims to various political and social actors. The results show high performance of ChatGPT for the baseline veracity evaluation task, with 72 percent of the cases evaluated correctly on average across languages without pre-training. Bing Chat performed worse with a 67 percent accuracy. We observe significant disparities in how chatbots evaluate prompts in high- and low-resource languages and how they adapt their evaluations to political communication concepts with ChatGPT providing more nuanced outputs than Bing Chat. Finally, we find that for some veracity detection-related tasks, the performance of chatbots varied depending on the topic of the statement or the source to which it is attributed. These findings highlight the potential of LLM-based chatbots in tackling different forms of false information in online environments, but also points to the substantial variation in terms of how such potential is realized due to specific factors, such as language of the prompt or the topic.
This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties. Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns cantered around corruption, incompetence and loadshedding.
Many socio-linguistic cues are used in conversational analysis, such as emotion, sentiment, and dialogue acts. One of the fundamental cues is politeness, which linguistically possesses properties such as social manners useful in conversational analysis. This article presents findings of polite emotional dialogue act associations, where we can correlate the relationships between the socio-linguistic cues. We confirm our hypothesis that the utterances with the emotion classes Anger and Disgust are more likely to be impolite. At the same time, Happiness and Sadness are more likely to be polite. A less expectable phenomenon occurs with dialogue acts Inform and Commissive which contain more polite utterances than Question and Directive. Finally, we conclude on the future work of these findings to extend the learning of social behaviours using politeness.
Tiancheng Hu, Manoel Horta Ribeiro, Robert West
et al.
According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as "said" and "told", as nonobjective quotatives, like "argued" and "insisted" would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? and 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were "moderate" centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over 7 years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans), and that this "quotative bias" is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.