Samantha Sudhoff, Pranav Perumal, Zhaoqing Wu
et al.
Climate discourse online plays a crucial role in shaping public understanding of climate change and influencing political and policy outcomes. However, climate communication unfolds across structurally distinct platforms with fundamentally different incentive structures: paid advertising ecosystems incentivize targeted, strategic persuasion, while public social media platforms host largely organic, user-driven discourse. Existing computational studies typically analyze these environments in isolation, limiting our ability to distinguish institutional messaging from public expression. In this work, we present a comparative analysis of climate discourse across paid advertisements on Meta (previously known as Facebook) and public posts on Bluesky from July 2024 to September 2025. We introduce an interpretable, end-to-end thematic discovery and assignment framework that clusters texts by semantic similarity and leverages large language models (LLMs) to generate concise, human-interpretable theme labels. We evaluate the quality of the induced themes against traditional topic modeling baselines using both human judgments and an LLM-based evaluator, and further validate their semantic coherence through downstream stance prediction and theme-guided retrieval tasks. Applying the resulting themes, we characterize systematic differences between paid climate messaging and public climate discourse and examine how thematic prevalence shifts around major political events. Our findings show that platform-level incentives are reflected in the thematic structure, stance alignment, and temporal responsiveness of climate narratives. While our empirical analysis focuses on climate communication, the proposed framework is designed to support comparative narrative analysis across heterogeneous communication environments.
Özgür Togay, Florian Kunneman, Javier Garcia-Bernardo
et al.
Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce discourse to coarse partisan labels, overlooking how these beliefs interact. This is especially evident in online political conversations, which are often nuanced and cover a wide range of subjects, making it difficult to automatically identify the target of discussion and the opinion expressed toward them. In this study, we investigate whether Large Language Models (LLMs) can address this challenge through Target-Stance Extraction (TSE), a recent natural language processing task that combines target identification and stance detection, enabling more granular analysis of political opinions. For this, we construct a dataset of 1,084 Reddit posts from r/NeutralPolitics, covering 138 distinct political targets and evaluate a range of proprietary and open-source LLMs using zero-shot, few-shot, and context-augmented prompting strategies. Our results show that the best models perform comparably to highly trained human annotators and remain robust on challenging posts with low inter-annotator agreement. These findings demonstrate that LLMs can extract complex political opinions with minimal supervision, offering a scalable tool for computational social science and political text analysis.
Ayoub Guemouria, Abdelghani Chehbouni, Salwa Belaqziz
et al.
The watershed represents a holistic system whose poor understanding of its multiple subsystems can lead to a pronounced water scarcity. This study aims to develop an innovative technique for managing water resources within the Souss-Massa watershed. It uses the System Dynamics (SD) methodology to analyze the interplay among the factors involved in water supply and demand. The results show that under the Business As Usual (BAU) scenario, water sustainability in this watershed is not assured. Groundwater drawdown (GWD) will increase significantly, with an estimated average decrease of −337 Mm3 for the period 2022 to 2050. To remedy this critical situation, several simulations were developed, each representing a distinct scenario. Scenario 1 improves irrigation efficiency by 10%, while scenario 2 achieves a 20% improvement. Scenario 3 builds on scenario 2 by doubling the volume of reused water. Scenario 4 extends scenario 3 by also doubling the volume of desalinated water. Scenario 5 combines the 10% improvement in irrigation efficiency from scenario 1 with a doubling of both reused and desalinated water volumes, along with a stabilization of irrigated areas. Scenario 6 adds a 7% increase in water supply to the measures in scenario 5. Finally, scenario 7 combines the 10% irrigation efficiency improvement from scenario 1 with a doubling of reused and desalinated water volumes, but reduces the irrigated area by 15%. This study is of crucial importance to decision-makers, as it provides them with strategies for promoting water-saving practices and, consequently, advancing the sustainable development agenda.
Urbanization. City and country, Political institutions and public administration (General)
This article analyzes the evolution of institutional reforms in the public administration system of the Republic of Armenia since its independence in 1991. In this context, it is taken into account that only the state is able to ensure the co-evolution of management technologies to achieve global goals and strategic objectives of the development of Armenian society. Technological dominants of political transformations, social and economic development require appropriate management adaptation, which is the focus of this study is the correct development and application of the functionality of decision support systems. Therefore, this study considers four stages of reforms: 1) Initial institutional creation from 1991 to 1999; 2) Formalization and adoption of Western governance models from 2000 to 2008; 3) European integration and administrative modernization from 2009 to 2017; 4) Political transformation accompanied by renewed administrative reforms since 2018. These reforms reflect a complex interplay of historical legacies, external influences, and internal aspirations for public administration modernization. Issues such as institutional inertia, limited localization of imported models, and political resistance are assessed in detail. In addition, the article compares these historical reforms with Armenia’s long-term goals outlined in the Public Administration Reform Strategy of the Republic of Armenia until 2030, emphasizing the transition from imitation reforms to sustainable institutional transformation.
Political science (General), Political institutions and public administration (General)
Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. This paper studies how LLMs are seemingly able to reflect more liberal versus more conservative viewpoints among other political perspectives in American politics. We show that LLMs possess linear representations of political perspectives within activation space, wherein more similar perspectives are represented closer together. To do so, we probe the attention heads across the layers of three open transformer-based LLMs (Llama-2-7b-chat, Mistral-7b-instruct, Vicuna-7b). We first prompt models to generate text from the perspectives of different U.S. lawmakers. We then identify sets of attention heads whose activations linearly predict those lawmakers' DW-NOMINATE scores, a widely-used and validated measure of political ideology. We find that highly predictive heads are primarily located in the middle layers, often speculated to encode high-level concepts and tasks. Using probes only trained to predict lawmakers' ideology, we then show that the same probes can predict measures of news outlets' slant from the activations of models prompted to simulate text from those news outlets. These linear probes allow us to visualize, interpret, and monitor ideological stances implicitly adopted by an LLM as it generates open-ended responses. Finally, we demonstrate that by applying linear interventions to these attention heads, we can steer the model outputs toward a more liberal or conservative stance. Overall, our research suggests that LLMs possess a high-level linear representation of American political ideology and that by leveraging recent advances in mechanistic interpretability, we can identify, monitor, and steer the subjective perspective underlying generated text.
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.
This research intends to explore how gender impacts different aspects of the quality of work life, emotional intelligence, and the work–family interface among professionals in the Indian construction industry. This study employs quantitative methodology using a questionnaire survey. The questionnaires were circulated to 900 construction professionals, and 724 valid responses were received, resulting in a response rate of 80.44%. The data were analyzed using descriptive analysis and independent sample t-Tests. The independent samples t-Test revealed significant (<i>p</i> < 0.05) gender disparities in various quality of work life factors, emotional intelligence, and work–family interface. Males had a more positive perception of career growth, management, and the working environment. In contrast, females experienced higher job satisfaction, work commitment, personal satisfaction towards their workplace and personal life, a higher work–family interface, and a better work–life balance. Females also experienced a higher level of physical exhaustion and had a higher level of emotional intelligence, while males experienced a higher level of mental exhaustion. There were no significant gender differences in satisfaction towards remuneration and fringe benefits, work culture, or the level of psychological exhaustion. The findings suggest that construction industry organizations could implement policies and practices that promote equal opportunities, provide support for work–family integration, and foster a culture of emotional intelligence. This research adds to the current body of knowledge by igniting novel empirical proof of gender-based differences in the Indian construction industry. It highlights the importance of addressing these disparities to improve the quality of work life, emotional intelligence, and work–family interface among professionals in the industry.
Political institutions and public administration (General)
Georgia Aitkenhead, Susanna Fantoni, James Scott
et al.
The moderation of user-generated content on online platforms remains a key solution to protecting people online, but also remains a perpetual challenge as the appropriateness of content moderation guidelines depends on the online community that they aim to govern. This challenge affects marginalized groups in particular, as they more frequently experience online abuse but also end up falsely being the target of content-moderation guidelines. While there have been calls for democratic, community-moderation, there has so far been little research into how to implement such approaches. Here, we present the co-creation of content moderation strategies with the users of an online platform to address some of these challenges. Within the context of AutSPACEs—an online citizen science platform that aims to allow autistic people to share their own sensory processing experiences publicly—we used a community-based and participatory approach to co-design a content moderation solution that would fit the preferences, priorities, and needs of its autistic user community. We outline how this approach helped us discover context-specific moderation dilemmas around participant safety and well-being and how we addressed those. These trade-offs have resulted in a moderation design that differs from more general social networks in aspects such as how to contribute, when to moderate, and what to moderate. While these dilemmas, processes, and solutions are specific to the context of AutSPACEs, we highlight how the co-design approach itself could be applied and useful for other communities to uncover challenges and help other online spaces to embed safety and empowerment.
Information technology, Political institutions and public administration (General)
Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false information. However, these characteristics tend to change dynamically across time, making it challenging to develop robust algorithms to fight misinformation spread. Therefore, this paper uses natural language processing to find common characteristics of political misinformation over a twelve year period. The results show that misinformation has increased dramatically in recent years and that it has increasingly started to be shared from sources with primary information modalities of text and images (e.g., Facebook and Instagram), although video sharing sources containing misinformation are starting to increase (e.g., TikTok). Moreover, it was discovered that statements expressing misinformation contain more negative sentiment than accurate information. However, the sentiment associated with both accurate and inaccurate information has trended downward, indicating a generally more negative tone in political statements across time. Finally, recurring misinformation categories were uncovered that occur over multiple years, which may imply that people tend to share inaccurate statements around information they fear or don't understand (Science and Medicine, Crime, Religion), impacts them directly (Policy, Election Integrity, Economic) or Public Figures who are salient in their daily lives. Together, it is hoped that these insights will assist researchers in developing algorithms that are temporally invariant and capable of detecting and mitigating misinformation across time.
Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond the two-party system in the US and audit Llama Chat in the context of EU politics in various settings to analyze the model's political knowledge and its ability to reason in context. We adapt, i.e., further fine-tune, Llama Chat on speeches of individual euro-parties from debates in the European Parliament to reevaluate its political leaning based on the EUandI questionnaire. Llama Chat shows considerable knowledge of national parties' positions and is capable of reasoning in context. The adapted, party-specific, models are substantially re-aligned towards respective positions which we see as a starting point for using chat-based LLMs as data-driven conversational engines to assist research in political science.
Thanasis Troboukis, Kelly Kiki, Antonis Galanopoulos
et al.
This chapter introduces a research project titled "Analyzing the Political Discourse: A Collaboration Between Humans and Artificial Intelligence", which was initiated in preparation for Greece's 2023 general elections. The project focused on the analysis of political leaders' campaign speeches, employing Artificial Intelligence (AI), in conjunction with an interdisciplinary team comprising journalists, a political scientist, and data scientists. The chapter delves into various aspects of political discourse analysis, including sentiment analysis, polarization, populism, topic detection, and Named Entities Recognition (NER). This experimental study investigates the capabilities of large language model (LLMs), and in particular OpenAI's ChatGPT, for analyzing political speech, evaluates its strengths and weaknesses, and highlights the essential role of human oversight in using AI in journalism projects and potentially other societal sectors. The project stands as an innovative example of human-AI collaboration (known also as "hybrid intelligence") within the realm of digital humanities, offering valuable insights for future initiatives.
In the study of the Political Resource Curse (Brollo et al.,2013), the authors identified a new channel to investigate whether the windfalls of resources are unambiguously beneficial to society, both with theory and empirical evidence. This paper revisits the framework with a new dataset. Specifically, we implemented a regression discontinuity design and difference-in-difference specification
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.
This article examines the factors of influence of relations between the protector state and the regional hegemon in terms of the resilience of the unrecognized state. The article is devoted to a comparative analysis of the lessons learned from the Nagorno-Karabakh war and non-peace.
Since the end of the Second World War new states have repeatedly emerged, secessions have occurred, and with them new conflicts. While some non-recognised states enjoy higher stability, others have great struggles in order to survive. Most of the literature focuses on the non-recognised states themselves and domestic factor, thus neglecting the role of global players as the regional hegemonn. The main objective of this paper is to find out whether hegemons (through the protector states) have an influence on the stability of the non-recognised states. A second alternative explanation emphasises the importance of the internal legitimacy of non-recognised states. Using the cases of Nagorno-Karabakh and Armenia, the study attempts to answer these questions through a qualitative analysis. The analysis of Armenia’s foreign policy between 1991-1992 and 2020 and the resilience around Nagorno-Karabakh is the core of the empirical part.
The results suggest that indeed relations between the hegemon and the protector state have an effect on the stability of the non-recognised state. A connection between the internal legitimacy of the non-recognised state and stability, on the other hand, cannot be concluded from the work. Despite the analytical function, the paper gives a good overview on the stability of non-recognised states, security policy and some of the post-communist conflicts.
Political science (General), Political institutions and public administration (General)
This study is a bibliometric analysis of urban studies publications from 2001 to 2021 that unravels the evolution and growing complexity of the field. Although developed regions still dominate and lead this area of inquiry, urban studies led by Asian scholars have increased dramatically over the last decade. There is also topic diffusion from developed regions to less-developed regions despite some unique emphases within each region caused by their local socio-economic-ecological contexts. Climate change adaptation and sustainable development, inequality, and urban governance are receiving growing attention globally. The findings suggest the rising importance of cross-continent knowledge transfer and multi-disciplinary collaboration, particularly among urban studies, sustainability policies and management, public administration, and development studies. Also, urban researchers need to pay more attention to issues faced by many growing cities in developing economies in Asia and Africa as more of the world's population will reside in those urban settings in the coming decades.
Urbanization. City and country, Political institutions and public administration (General)
La probablemente necesaria discrecionalidad (con respeto a los límites legales) con la que los grupos políticos locales disponen de las aportaciones financieras de su respectiva entidad local «para su funcionamiento» ha estado, a menudo, acompañada de un pacto tácito de opacidad, lo que ha venido siendo un serio déficit de nuestra democracia local. Frente a esta praxis, debe subrayarse el elevado interés público en la divulgación de esta información, directamente vinculada al mandato de rendición de cuentas y reforzada por el carácter representativo de los beneficiarios de estas subvenciones, pues no otra es su naturaleza.
Political institutions and public administration (General), Accounting. Bookkeeping
Political polling is a multi-billion dollar industry with outsized influence on the societal trajectory of the United States and nations around the world. However, it has been challenged by factors that stress its cost, availability, and accuracy. At the same time, artificial intelligence (AI) chatbots have become compelling stand-ins for human behavior, powered by increasingly sophisticated large language models (LLMs). Could AI chatbots be an effective tool for anticipating public opinion on controversial issues to the extent that they could be used by campaigns, interest groups, and polling firms? We have developed a prompt engineering methodology for eliciting human-like survey responses from ChatGPT, which simulate the response to a policy question of a person described by a set of demographic factors, and produce both an ordinal numeric response score and a textual justification. We execute large scale experiments, querying for thousands of simulated responses at a cost far lower than human surveys. We compare simulated data to human issue polling data from the Cooperative Election Study (CES). We find that ChatGPT is effective at anticipating both the mean level and distribution of public opinion on a variety of policy issues such as abortion bans and approval of the US Supreme Court, particularly in their ideological breakdown (correlation typically >85%). However, it is less successful at anticipating demographic-level differences. Moreover, ChatGPT tends to overgeneralize to new policy issues that arose after its training data was collected, such as US support for involvement in the war in Ukraine. Our work has implications for our understanding of the strengths and limitations of the current generation of AI chatbots as virtual publics or online listening platforms, future directions for LLM development, and applications of AI tools to the political domain. (Abridged)
We use instruction-tuned Large Language Models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.
In this paper, using Monte Carlo simulations we show that the Blume-Capel model gives rise to the social depolarization. This model borrowed from statistical physics uses the continuous Ising spin varying from -1 to 1 passing by zero to express the political stance of an individual going from ultra-left (-1) to ultra-right (+1). The particularity of the Blume-Capel model is the existence of a $D$-term which favors the state of spin zero which is a neutral stance. We consider the political system of the USA where voters affiliate with two political groups: Democrats or Republicans, or are independent. Each group is composed of a large number of interacting members of the same stance. We represent the general political ambiance (or degree of social turmoil) with a temperature $T$ similar to thermal agitation in statistical physics. When three groups interact with each other, their stances can get closer or further from each other, depending on the nature of their inter-group interactions. We study the dynamics of such variations as functions of the value of the $D$-term of each group. We show that the polarization decreases with increasing $D$. We outline the important role of $T$ in these dynamics. These MC results are in excellent agreement with the mean-field treatment of the same model.