Hasil untuk "Political Science"

Kma Solaiman

We present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and enriched with rationale indicators. The annotation pipeline incorporates targeted worker qualification and was refined through pilot-phase analysis. We quantify inter-annotator agreement, analyze misalignment with source-level outlet bias, and organize the resulting labels into interpretable subsets. Additionally, we simulate annotation using schema-constrained GPT-4o, enabling direct comparison to human labels and revealing mirrored asymmetries, especially in misclassifying subtly right-leaning content. We define two modeling tasks: perception drift prediction and rationale type classification, and report baseline performance to illustrate the challenge of explainable bias detection. BiasLab's rich rationale annotations provide actionable interpretations that facilitate explainable modeling of political bias, supporting the development of transparent, socially aware NLP systems. We release the dataset, annotation schema, and modeling code to encourage research on human-in-the-loop interpretability and the evaluation of explanation effectiveness in real-world settings.

Alessio Pittiglio

In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates. Our approach uses pretrained Transformer-based models and proposes several ways to leverage context. In the fallacy classification subtask, our models achieved macro F1-scores of 0.4444 (text), 0.3559 (audio), and 0.4403 (multimodal). Our multimodal model showed performance comparable to the text-only model, suggesting potential for improvements.

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.

Ricardo Alonzo Fernández Salguero

The 2008 global financial crisis marked the beginning of a decade dominated by fiscal austerity policies in much of the developed world. This paper presents a qualitative narrative review of an extensive collection of academic literature to synthesize evidence on the multifaceted effects of austerity. Following a thematic approach inspired by PRISMA guidelines, the economic, social, and political consequences of these measures are examined. The analysis reveals a majority consensus regarding the recessive effects of austerity, especially when implemented during economic crises, with negative fiscal multipliers that often exacerbate GDP contraction. Socially, austerity is associated with rising inequality, negative impacts on public health, disproportionate gender consequences, and a weakening of social safety nets. Politically, evidence links austerity to the erosion of trust in institutions, a rise in populism, and electoral instability. Despite the political narrative presenting austerity as an inevitable necessity for fiscal sustainability, academic literature underscores its high costs and questionable efficacy, advocating for more contextualized and equitable economic policy approaches.

Renqi Chen, Haoyang Su, Shixiang Tang et al.

The Science of Science (SoS) explores the mechanisms underlying scientific discovery, and offers valuable insights for enhancing scientific efficiency and fostering innovation. Traditional approaches often rely on simplistic assumptions and basic statistical tools, such as linear regression and rule-based simulations, which struggle to capture the complexity and scale of modern research ecosystems. The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS, enabling the automation of large-scale pattern discovery and uncovering insights previously unattainable. This paper offers a forward-looking perspective on the integration of Science of Science with AI for automated research pattern discovery and highlights key open challenges that could greatly benefit from AI. We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them. Additionally, we present a preliminary multi-agent system as an illustrative example to simulate research societies, showcasing AI's ability to replicate real-world research patterns and accelerate progress in Science of Science research.

Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra

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

William F. Godoy, Oscar Hernandez, Paul R. C. Kent et al.

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReXCastro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era.

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.

Kobi Hackenburg, Ben M. Tappin, Paul Röttger et al.

Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment (N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence of a log scaling law: model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are barely more persuasive than models smaller in size by an order of magnitude or more. Second, mere task completion (coherence, staying on topic) appears to account for larger models' persuasive advantage. These findings suggest that further scaling model size will not much increase the persuasiveness of static LLM-generated messages.

Michael L Brodie

Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution.

N. Werner, J. Řípa, C. Thöne et al.

This is the first in a collection of three papers introducing the science with an ultra-violet (UV) space telescope on an approximately 130~kg small satellite with a moderately fast re-pointing capability and a real-time alert communication system approved for a Czech national space mission. The mission, called Quick Ultra-Violet Kilonova surveyor - QUVIK, will provide key follow-up capabilities to increase the discovery potential of gravitational wave observatories and future wide-field multi-wavelength surveys. The primary objective of the mission is the measurement of the UV brightness evolution of kilonovae, resulting from mergers of neutron stars, to distinguish between different explosion scenarios. The mission, which is designed to be complementary to the Ultraviolet Transient Astronomy Satellite - ULTRASAT, will also provide unique follow-up capabilities for other transients both in the near- and far-UV bands. Between the observations of transients, the satellite will target other objects described in this collection of papers, which demonstrates that a small and relatively affordable dedicated UV-space telescope can be transformative for many fields of astrophysics.

Dimosthenis Antypas, Alun Preece, Jose Camacho-Collados

Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion. In the same vein, politicians use Twitter to express their opinions, debate among others on current topics and promote their political agendas aiming to influence voter behaviour. In this paper, we attempt to analyse tweets of politicians from three European countries and explore the virality of their tweets. Previous studies have shown that tweets conveying negative sentiment are likely to be retweeted more frequently. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets collected from members of parliament in Greece, Spain and the United Kingdom, including devolved administrations. We achieved this by systematically exploring and analysing the differences between influential and less popular tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times, and highlights interesting differences between political parties as well as between politicians and the general population.

Shikhar Kumar, Eliran Itzhak, Samuel Olatunji et al.

Aiming to explore the impact of politeness on Human robot interaction, this study tested varying levels of politeness in a human robot collaborative table setting task. Polite behaviour was designed based on the politeness rules of Lakoff. A graphical user interface was developed for the interaction with the robot offering three levels of politeness, and an experiment was conducted with 20 older adults and 30 engineering students. Results indicated that the quality of interaction was influenced by politeness as participants significantly preferred the polite mode of the robot. However, the older adults were less able to distinguish between the three politeness levels. Future studies should thus include pre experiment training to increase the familiarity of the older adults with robotic technology. These studies should also include other permutations of the politeness rules of Lakoff.

Christopher Thomas, Adriana Kovashka

The news media shape public opinion, and often, the visual bias they contain is evident for human observers. This bias can be inferred from how different media sources portray different subjects or topics. In this paper, we model visual political bias in contemporary media sources at scale, using webly supervised data. We collect a dataset of over one million unique images and associated news articles from left- and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines. We also present extensive qualitative results demonstrating the nuances of the data.