Hasil untuk "Political science"

R. Perrucci, C. Perrucci, M. Subramaniam

T. Gieryn

S. Jasanoff

S. Lipset

F. Greenstein, N. Polsby

Eric Msughter Aondover, Ifedolapo Ademosu, Ramson Oloche Acheme

The 2023 general elections in Nigeria were characterised by a surge in hate speech, particularly across digital platforms, significantly shaping the political landscape and influencing voter behaviour. The study highlights how ethno-religious and politically motivated hate speech deepened societal divisions, fostered misinformation, and contributed to voter apathy and fear-driven electoral choices. On social media platforms, individuals and organisations believe that freedom of speech entitles them to speak their minds without any restrictions whatsoever. During elections, this freedom of expression plays out without any hindrance, pervading social media platforms with hate speech rhetoric, misinformation, and disinformation. This study examines how voters’ exposure to political hate speech during the 2023 presidential election campaigns, as disseminated through traditional media, social platforms, and campaign rhetoric, shaped the attitude of voters, their trust in the ability of the candidates to deliver, and their level of electoral participation. Using the Functional Theory of Campaign Discourse, the study analyses the system through which inflammatory language divides public opinion, reinforces divisions in political party groups (among supporters), and destroys the confidence voters have in the Nigerian electoral processes. Based on the pragmatic approach of research design, survey method, and content analysis of hate speech in the 2023 presidential election campaigns will be adopted, and results show pervasive use of hate speech by the political class and how this results in low voter turnout.

J. Box-Steffensmeier, B. Jones

A. A. Blinova, M. A. Pirogov, I. M. Shevchenko et al.

As part of this work, a computer quantum chemical simulation of the interaction of magnesium phosphate with essential amino acids was carried out in order to determine the optimal stabilizer for Mg3(PO4)2 nanoparticles. Quantum chemical modeling was carried out using the QChem software and the IQmol molecular editor. At the first stage, the modeling of the magnesium phosphate molecule and the molecules of essential amino acids was carried out, then the modeling of the molecular complex "amino acid- Mg3(PO4)2" was considered, in which the interaction of magnesium phosphate with an amino acid passed through an amino group. As a result, models of molecular complexes were obtained, and the values of the total energy of the molecular complex, the energies of the highest populated and lowest free molecular orbitals, chemical rigidity and the difference in the total energy of the amino acid and the molecular complex "amino acid- Mg3(PO4)2" were calculated. As a result, it was found that essential amino acids can be effective stabilizers of magnesium phosphate nanoparticles, which is confirmed by the values of the difference in total energy and chemical rigidity of molecular complexes. Due to the fact that the molecular complex of tryptophan and magnesium phosphate, in which the interaction of molecules occurs through the amino group in the in the indole ring of tryptophan, has the highest values of the difference in the total energy (∆E = 1946,223 kcal/mol) and chemical hardness (ε = 0.121 eV), it can be concluded that tryptophan is the optimal stabilizer for nanoparticles magnesium phosphate.

Kamila Szymańska

Celem artykułu jest zdekonstruowanie wybranych narracji Władimira Putina o Ukrainie w oparciu o dostępną typologię narracji strategicznych. Pozwala to na wyjście poza wierzchnią warstwę komunikatów, których treść ewoluuje, dynamicznie dostosowując się do potrzeb nadawcy. Komunikacja strategiczna zawiera w sobie informacje o celach nadawcy, które są nadrzędne wobec samych narracji. Kreowane przez Putina mity o Ukrainie skupiały się przede wszystkim na aspekcie tożsamościowym i systemowym, a ich pochodną były narracje dotyczące problematyzowanych kwestii, jak integracja Ukrainy z Unią Europejską i aneksja jej terytoriów. Rezultatem pośrednim, który narracje miały wywołać wśród odbiorców, było osłabienie tożsamości i podmiotowości Ukrainy w stosunkach międzynarodowych. Oddziałując na tożsamość, Putin wpływał na interesy odbiorców tak, by te były zbieżne z jego strategicznymi celami. Zrozumienie celów, a także skutków pośrednich, jest kluczowe, gdyż sama treść narracji z założenia je maskuje.

Brian Wright, Peter Alonzi, Ali Rivera

The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.

Christina P. Walker, Daniel S. Schiff, Kaylyn Jackson Schiff

This article presents the Political Deepfakes Incidents Database (PDID), a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.' The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.

Joseba Fernandez de Landa, Rodrigo Agerri

Social media users express their political preferences via interaction with other users, by spontaneous declarations or by participation in communities within the network. This makes a social network such as Twitter a valuable data source to study computational science approaches to political learning inference. In this work we focus on three diverse regions in Spain (Basque Country, Catalonia and Galicia) to explore various methods for multi-party categorization, required to analyze evolving and complex political landscapes, and compare it with binary left-right approaches. We use a two-step method involving unsupervised user representations obtained from the retweets and their subsequent use for political leaning detection. Comprehensive experimentation on a newly collected and curated dataset comprising labeled users and their interactions demonstrate the effectiveness of using Relational Embeddings as representation method for political ideology detection in both binary and multi-party frameworks, even with limited training data. Finally, data visualization illustrates the ability of the Relational Embeddings to capture intricate intra-group and inter-group political affinities.

Ozan Evkaya, Miguel de Carvalho

As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.

Sangyeon Kim

As new technologies rapidly reshape patterns of political communication, platforms like Twitch are transforming how people consume political information. This entertainment-oriented live streaming platform allows us to observe the impact of technologies such as ``live-streaming'' and ``streaming-chat'' on political communication. Despite its entertainment focus, Twitch hosts a variety of political actors, including politicians and pundits. This study explores Twitch politics by addressing three main questions: 1) Who are the political Twitch streamers? 2) What content is covered in political streams? 3) How do audiences of political streams interact with each other? To identify political streamers, I leveraged the Twitch API and supervised machine-learning techniques, identifying 574 political streamers. I used topic modeling to analyze the content of political streams, revealing seven broad categories of political topics and a unique pattern of communication involving context-specific ``emotes.'' Additionally, I created user-reference networks to examine interaction patterns, finding that a small number of users dominate the communication network. This research contributes to our understanding of how new social media technologies influence political communication, particularly among younger audiences.

Sagi Pendzel, Nir Lotan, Alon Zoizner et al.

The rise of social media has been argued to intensify uncivil and hostile online political discourse. Yet, to date, there is a lack of clarity on what incivility means in the political sphere. In this work, we utilize a multidimensional perspective of political incivility, developed in the fields of political science and communication, that differentiates between impoliteness and political intolerance. We present state-of-the-art incivility detection results using a large dataset of 13K political tweets, collected and annotated per this distinction. Applying political incivility detection at large-scale, we observe that political incivility demonstrates a highly skewed distribution over users, and examine social factors that correlate with incivility at subpopulation and user-level. Finally, we propose an approach for modeling social context information about the tweet author alongside the tweet content, showing that this leads to improved performance on the task of political incivility detection. We believe that this latter result holds promise for socially-informed text processing in general.

Mónica Melero Lázaro

Reseña con breve descripción de los contenidos que se desarrollan en la obra ‘La transparencia de los partidos políticos (2016-2019). Entre la estrategia de comunicación y su apertura efectiva’, escrita por la Doctora María Díez Garrido y publicada por la colección de monografías del Congreso de los Diputados en 2021. Se trata de un monográfico que analiza el estado actual de la transparencia en los partidos políticos españoles desde el punto de vista de la comunicación y la política durante las elecciones celebradas entre 2016 y 2019. La obra aporta una visión general y completa de la transparencia en la política, además de presentar las pautas necesarias para elaborar una Guía de buenas prácticas para partidos políticos abiertos.  

V. A. Traag

Citations in science are being studied from several perspectives, among which approaches such as scientometrics and science of science. In this chapter I briefly review some of the literature on citations, citation distributions and models of citations. These citations feature prominently in another part of the literature which is dealing with research evaluation and the role of metrics and indicators in that process. Here I briefly review part of the discussion in research evaluation. This also touches on the subject of how citations relate to peer review. Finally, I conclude by trying to integrate the two literatures. The fundamental problem in research evaluation is that research quality is unobservable. This has consequences for conclusions that we can draw from quantitative studies of citations and citation models. The term ``indicators'' is a relevant concept in this context, which I try to clarify. Causality is important for properly understanding indicators, especially when indicators are used in practice: when we act on indicators, we enter causal territory. Even when an indicator might have been valid, through its very use, the consequences of its use may invalidate it. By combining citation models with proper causal reasoning and acknowledging the fundamental problem about unobservable research quality, we may hope to make progress.

R. Franzese, Jude C. Hays

Frank R. Baumgartner

R. Goodin, H. Klingemann