Hasil untuk "Sociology"

S. Lipset

Jodi O’Brien, M. Lamont

C. Worth

Jay A Conger, R. N. Kanungo

R. Nisbet, K. Erikson

H. Becker

Miguel Ballesteros, Ramsés H. Mena, Arno Siri-Jégousse et al.

The Curie-Weiss model is used to study phase transitions in statistical mechanics and has been the object of rigorous analysis in mathematical physics. We analyse the problem of reconstructing the probability measure of a multi-group Curie-Weiss model from a sample of data by employing the maximum likelihood estimator for the coupling parameters of the model, under the assumption that there is interaction within each group but not across group boundaries. The estimator has a number of positive properties, such as consistency, asymptotic normality, and exponentially decaying probabilities of large deviations of the estimator with respect to the true parameter value. A shortcoming in practice is the necessity to calculate the partition function of the Curie-Weiss model, which scales exponentially with respect to the population size. There are a number of applications of the estimator in political science, sociology, and automated voting, centred on the idea of identifying the degree of social cohesion in a population. In these applications, the coupling parameter is a natural way to quantify social cohesion. We treat the estimation of the optimal weights in a two-tier voting system, which requires the estimation of the coupling parameter.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda

Humour has long been recognized as a key factor in enhancing creativity, group effectiveness, and employee well-being across various domains. However, its occurrence and impact within software engineering (SE) teams remains under-explored. This paper introduces a comprehensive, literature review-based taxonomy exploring the characterisation and use of humour in SE teams, with the goal of boosting productivity, improving communication, and fostering a positive work environment while emphasising the responsible use of humour to mitigate its potential negative impacts. Drawing from a wide array of studies in psychology, sociology, and organizational behaviour, our proposed framework categorizes humour into distinct theories, styles, models, and scales, offering SE professionals and researchers a structured approach to understanding humour in their work. This study also addresses the unique challenges of applying humour in SE, highlighting its potential benefits while acknowledging the need for further empirical validation in this context. Ultimately, our study aims to pave the way for more cohesive, creative, and psychologically supportive SE environments through the strategic use of humour.

Julien Wacquez, Elisabetta Zibetti, Joffrey Becker et al.

As robots find their way into more and more aspects of everyday life, questions around trust are becoming increasingly important. What does it mean to trust a robot? And how should we think about trust in relationships that involve both humans and non-human agents? While the field of Human-Robot Interaction (HRI) has made trust a central topic, the concept is often approached in fragmented ways. At the same time, established work in sociology, where trust has long been a key theme, is rarely brought into conversation with developments in robotics. This article argues that we need a more interdisciplinary approach. By drawing on insights from both social sciences and social robotics, we explore how trust is shaped, tested and made visible. Our goal is to open up a dialogue between disciplines and help build a more grounded and adaptable framework for understanding trust in the evolving world of human-robot interaction.

José Vicente González Orozco, Sara Cortés Gómez

Introducción: La comunicación y la información han ido evolucionando con el paso del tiempo. La entrada de la red e Internet han generado una transformación en el paradigma de la comunicación, hasta el punto en que, los medios tradicionales como la radio, la prensa o la televisión, se han visto superados por la entrada de nuevas redes sociales. Entre estas se encuentran las conocidas como plataformas de streaming. Son uno de los principales núcleos modernos. Sitio elegido por importantes comunicadores para dar a conocer la información y generar una amplía comunidad pública, donde poder llegar a un marco más global. La presente investigación analiza el desarrollo de la comunicación deportiva dentro del mundo del fútbol, seleccionando como propuesta, una de las redes sociales más influyentes dentro del ámbito de la red, Twitch. Y, los canales más influyentes en España: “Gerard Romero”, “RubenMartinweb” y “El ChiringuitoTV”. Metodología: Para el correcto desarrollo de la investigación se ha utilizado una metodología mixta. Basada en una parte cuantitativa y cualitativa del fenómeno para abarcar distintas perspectivas y comprender el conjunto de los datos adquiridos con el objetivo de conseguir resultados verídicos y enriquecedores. Por ello se ha dividido en tres partes: Un estudio del origen e influencia de los estudios de caso, la transformación y el desarrollo de la plataforma con respecto a los canales y los momentos más relevantes e importantes para la audiencia. Resultados: Los resultados muestran la transformación vivida por la comunicación deportiva dentro del mundo del fútbol a lo largo de los años. A su vez, se observa la entrada de las redes sociales y las plataformas de streaming en el fenómeno, junto a la influencia de las nuevas figuras comunicativas encargadas de dar a conocer lo ocurrido. Discusión y conclusiones: Se considera el crecimiento vivido por parte de la comunicación deportiva dentro del fútbol y se reflexiona sobre las razones de su expansión, junto al futuro más próximo.

Arwin Nemani, Schahryar Kananian, Annabelle Starck et al.

Abstract Background Refugees and asylum seekers encounter numerous post-migration living difficulties (PMLDs) that can substantially affect their mental health. However, the role of PMLDs remains insufficiently explored, particularly in clinical refugee populations. This study aimed to identify subgroups based on patterns of PMLD by examining their relationship with depressive symptoms and determining which stressors function as key bridges. Methods This study reports a secondary analysis of baseline data from the ReTreat trial. Data were collected from 141 refugees and asylum seekers enrolled in a multicentre randomized controlled trial of a culturally adapted CBT program in Germany. Participants completed measures of depressive symptoms (PHQ-9) and post-migration stressors (27-item checklist). Latent Profile Analysis (LPA) was used to identify distinct burden profiles. Exploratory Factor Analysis (EFA) examined the dimensionality of PMLDs. Network analysis was conducted to investigate symptom–stressor connectivity. Results Three latent profiles emerged: Class 1 showed elevated distress across all domains; Class 2 was characterized by family separation and homesickness; and Class 3 exhibited minimal post-migration stress. EFA of PMLDS supported a four-factor solution: institutional/legal stressors, structural hardship, health/service access, and emotional/family-related strain. Depressive symptoms differed significantly across profiles, with highest scores in the high burden group (Class 1). Network analysis identified institutional/legal and emotional/family-related stressors as central bridge nodes linking PMLDs to depressive symptoms. Conclusions PMLDs are multidimensional and heterogeneously distributed among forcibly displaced individuals. Legal insecurity and emotional strain are particularly influential in connecting environmental hardship to depressive symptoms. Trial registration This study uses baseline data from a registered randomized controlled trial (DRKS00021536).

Wenqin Du, Yuan Zhang, Wen Zhou

Testing network effects in weighted directed networks is a foundational problem in econometrics, sociology, and psychology. Yet, the prevalent edge dependency poses a significant methodological challenge. Most existing methods are model-based and come with stringent assumptions, limiting their applicability. In response, we introduce a novel, fully nonparametric framework that requires only minimal regularity assumptions. While inspired by recent developments in $U$-statistic literature (arXiv:1712.00771, arXiv:2004.06615), our approach notably broadens their scopes. Specifically, we identified and carefully addressed the challenge of indeterminate degeneracy in the test statistics $-$ a problem that aforementioned tools do not handle. We established Berry-Esseen type bound for the accuracy of type-I error rate control. Using original analysis, we also proved the minimax optimality of our test's power. Simulations underscore the superiority of our method in computation speed, accuracy, and numerical robustness compared to competing methods. We also applied our method to the U.S. faculty hiring network data and discovered intriguing findings.

Alexei Pisacane, Victor-Alexandru Darvariu, Mirco Musolesi

Graph path search is a classic computer science problem that has been recently approached with Reinforcement Learning (RL) due to its potential to outperform prior methods. Existing RL techniques typically assume a global view of the network, which is not suitable for large-scale, dynamic, and privacy-sensitive settings. An area of particular interest is search in social networks due to its numerous applications. Inspired by seminal work in experimental sociology, which showed that decentralized yet efficient search is possible in social networks, we frame the problem as a collaborative task between multiple agents equipped with a limited local view of the network. We propose a multi-agent approach for graph path search that successfully leverages both homophily and structural heterogeneity. Our experiments, carried out over synthetic and real-world social networks, demonstrate that our model significantly outperforms learned and heuristic baselines. Furthermore, our results show that meaningful embeddings for graph navigation can be constructed using reward-driven learning.

Jianxiang Wang, Can M. Le, Tianxi Li

Network-linked data, where multivariate observations are interconnected by a network, are becoming increasingly prevalent in fields such as sociology and biology. These data often exhibit inherent noise and complex relational structures, complicating conventional modeling and statistical inference. Motivated by empirical challenges in analyzing such data sets, this paper introduces a family of network subspace generalized linear models designed for analyzing noisy, network-linked data. We propose a model inference method based on subspace-constrained maximum likelihood, which emphasizes flexibility in capturing network effects and provides a robust inference framework against network perturbations. We establish the asymptotic distributions of the estimators under network perturbations, demonstrating the method's accuracy through extensive simulations involving random network models and deep-learning-based embedding algorithms. The proposed methodology is applied to a comprehensive analysis of a large-scale study on school conflicts, where it identifies significant social effects, offering meaningful and interpretable insights into student behaviors.

Melik Onur Güzel, Eşref Ay, Ozan Çatir

Uzungöl is a popular natural route and a major tourist attraction in Turkey, but recently overtourism has become a major problem. With the increase in the number of visitors, rapid construction, concreting and various forms of pollution have had a negative impact on the destination of Uzungöl and its environment. The aim of this study is to reveal the evidence for the existence of overtourism in Uzungöl through visitors’ reviews on online platforms and to determine in which areas overtourism is effective. In this study, which was conducted using an exploratory approach, it was observed that many visitors reviewed on overtourism in Uzungöl using TripAdvisor. These reviews were analysed using content analysis. As a result of the analysis, it was found that visitors’ reviews focused on three themes related to overtourism in Uzungöl. These are environmental, economic, and socio-cultural themes. As a result of the study, it was determined that environmental concerns are more prominent in terms of overtourism. In addition, it was understood that tourists visiting the region have a negative image in terms of overbuilding, concretisation, and unplanned urbanisation.

David Oluseun Olayungbo, Aziza Zhuparova, Mamdouh Abdulaziz Saleh Al-Faryan et al.

The relationship between oil price movements and stock markets during the COVID-19 pandemic and the geopolitical crisis like the ongoing Russian-Ukraine war is yet unexplored extensively. This study therefore examines the return-correlation effects of oil prices on stock markets and their spillover effects in oil-exporting and European countries using daily closing data. After estimating the GARCH process, we employ the static and dynamic Markov Switching model that allow the relationship between oil price and stock market to switch between two regimes coined the COVID-19 and the Russia-Ukraine war periods. The static model shows stock price returns to respond positively and significantly to oil price returns in Italy, Germany and the US during the Covid-19 period while the response is significantly positive only for US in the Russia-Ukraine war period. As regards the volatility spillover, significant spillover is found from stock to oil market for Nigeria, vice versa for Saudi Arabia and bi-directional volatility spillover found for the US, Italy and Germany during the COVID-19 period. The policy implication is that Nigeria and Saudi Arabia should prioritize financial policy and energy policy respectively while US, Italy and Germany should adopt policy coordination to stabilize oil-stock market volatility during low oil price period like the COVID-19 period.

Orlando E. Martínez-Durive, Sachit Mishra, Cezary Ziemlicki et al.

Digital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation is represented by the limited availability of dependable digital datasets, especially in the context of data gathered by mobile network operators or service providers, due to concerns about user privacy and industrial competition. The resulting lack of reference datasets curbs the production of new research methods and results, and prevents verifiability and reproducibility of research outcomes. The NetMob23 dataset offers a rare opportunity to the multidisciplinary research community to access rich data about the spatio-temporal consumption of mobile applications in a developed country. The generation process of the dataset sets a new quality standard, leading to information about the demands generated by 68 popular mobile services, geo-referenced at a high resolution of $100\times100$ $m^2$ over 20 metropolitan areas in France, and monitored during 77 consecutive days in 2019.

Linzhuang Sun, Yao Dong, Nan Xu et al.

The development of Large Language Models (LLMs) provides human-centered Artificial General Intelligence (AGI) with a glimmer of hope. Empathy serves as a key emotional attribute of humanity, playing an irreplaceable role in human-centered AGI. Despite numerous researches aim to improve the cognitive empathy of models by incorporating external knowledge, there has been limited attention on the sensibility and rationality of the conversation itself, which are vital components of the empathy. However, the rationality information within the conversation is restricted, and previous methods of extending knowledge are subject to semantic conflict and single-role view. In this paper, we design an innovative encoder module inspired by self-presentation theory in sociology, which specifically processes sensibility and rationality sentences in dialogues. And we employ a LLM as a rational brain to decipher profound logical information preserved within the conversation, which assists our model in assessing the balance between sensibility and rationality to produce high-quality empathetic response. Experimental results demonstrate that our model outperforms other methods in both automatic and human evaluations.

Giuseppe Masullo, Marianna Coppola

IntroductionThis work aims at transposing ethnographic research into digital contexts to probe its potential and limitations in a specific field of study: that of sexuality, particularly suited to ethnographic exploration. We chose as our case study a web community of Italian asexual people. As we shall see, this allowed us to simultaneously explore both the various techniques called into play in digital ethnography and the digital as a specific sphere within which sexuality takes on a very peculiar meaning. Digital sociality is paramount for the definition of imaginaries, meanings, and practices that could not be explored elsewhere. This is due to the implicit characteristics of the population studied, which does not find corresponding physical spaces of aggregation.MethodsThe paper will present the research design using this specific case study to address some of the typical dilemmas that researchers face when following the digital ethnographic approach and will explore the research results as an example of the kind of analysis available with the information and data collected through this method.Results and discussionThe conclusions will attempt to briefly outline the shortfalls and advantages of this method, considering its application to this specific field of study.

Marco Conti, Andrea Passarella

The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation Internet. It will require a radical change of the Internet data-management paradigm, from the current platform-centric to a human-centric model. In this paper we present a new paradigm for Internet data management that we name Internet of People (IoP) because it embeds human behavior models in its algorithms. To this end, IoP algorithms exploit quantitative models of the humans' individual and social behavior, from sociology, anthropology, psychology, economics, physics. IoP is not a replacement of the current Internet networking infrastructure, but it exploits legacy Internet services as (reliable) primitives to achieve end-to-end connectivity on a global-scale. In this opinion paper, we first discuss the key features of the IoP paradigm along with the underlying research issues and challenges. Then, we present emerging data-management paradigms that are anticipating IoP.