Hasil untuk "Socialism. Communism. Anarchism"

Carlos M. Herrera

Este artículo se propone analizar las dirigencias femeninas que surgieron en el Partido socialista a partir del cruce de tres ejes: evolución de las instancias partidarias, la mutación de consignas y programa y las vicisitudes socio-personales de las protagonistas. El artículo defiende la tesis de un cambio importante a partir de 1945.

Marcelo Hernández

Reseña de Evangelisti, Valerio. One Big Union. Un gran sindicato. España: Hoja de Lata, 2024, 442 pgs.

April Clarke

Productive software engineering teams require effective communication and balanced contributions between team members. However, teams are often ineffective at these skills, which is detrimental to project success. Project-based university courses are an opportunity for students to practise these skills, but we have yet to establish how we can guide students towards improving their communication and coordination. We aim to develop project management tool features, informed by social network analysis, that nudge students in software engineering group projects towards beneficial behaviours. To do this, we will first evaluate the suitability of social network analysis techniques for identifying areas of improvement in teams' communication. Then, we will develop features in a project management tool that aid students in identifying and addressing these areas of improvement, and evaluate them in the context of a software engineering group project.

Qianjie Wei, Xiaoying Wei, Yiqi Liang et al.

The decline of social connectedness caused by distance and physical limitations severely affects older adults' well-being and mental health. While virtual reality (VR) is promising for older adults to socialize remotely, existing social VR designs primarily focus on verbal communication (e.g., reminiscent, chat). Actively engaging in shared activities is also an important aspect of social connection. We designed RemoteChess, which constructs a social community and a culturally relevant activity (i.e., Chinese chess) for older adults to play while engaging in social interaction. We conducted a user study with groups of older adults interacting with each other through RemoteChess. Our findings indicate that RemoteChess enhanced participants' social connectedness by offering familiar environments, culturally relevant social catalysts, and asymmetric interactions. We further discussed design guidelines for designing culturally relevant social activities in VR to promote social connectedness for older adults.

Pawat Akara-pipattana, Sergei Nechaev, Bogdan Slavov

Digitally connected societies approach a \enquote{transparent} regime where all agents can interact without geographic or social barriers -- a limit realized by complete graph topologies. We solve exactly a $q$-state Potts model with many-body interactions on this geometry, modeling agents from $q$ distinct communities. Analyzing the illustrative case of competing pairwise and three-body couplings, we identify three key phases in the thermodynamic limit: democratic (all communities equal), marginalized ($q-1$ communities surviving), and consensus (one dominant group). For two-community systems, we identify a special coupling regime where interaction energies cancel, yielding purely entropy-driven dynamics -- a statistical physics representation of atomized societies without structured influence. Monte Carlo simulations confirm these phases and reveal metastable switching dynamics in finite systems. Furthermore, we establish an exact correspondence between this social model and mean-field $SU(N)$ quantum spin systems with quadratic and cubic Casimir interactions, revealing a \enquote{social-quantum} duality. This duality enables quantitative classification of social structures via Young diagrams and reinterprets quantum symmetry breaking as opinion stratification, bridging statistical sociology and quantum many-body physics.

Simon Münker, Nils Schwager, Achim Rettinger

The ability of Large Language Models (LLMs) to mimic human behavior triggered a plethora of computational social science research, assuming that empirical studies of humans can be conducted with AI agents instead. Since there have been conflicting research findings on whether and when this hypothesis holds, there is a need to better understand the differences in their experimental designs. We focus on replicating the behavior of social network users with the use of LLMs for the analysis of communication on social networks. First, we provide a formal framework for the simulation of social networks, before focusing on the sub-task of imitating user communication. We empirically test different approaches to imitate user behavior on X in English and German. Our findings suggest that social simulations should be validated by their empirical realism measured in the setting in which the simulation components were fitted. With this paper, we argue for more rigor when applying generative-agent-based modeling for social simulation.

Zhang Zheng, Deheng Ye, Peilin Zhao et al.

Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on information processing and strategy selection, overlooking the significance of persuasive communication in influencing other players' beliefs and responses. In SDGs, success depends not only on making correct deductions but on convincing others to response in alignment with one's intent. To address this limitation, we formalize turn-based dialogue in SDGs as a Stackelberg competition, where the current player acts as the leader who strategically influences the follower's response. Building on this theoretical foundation, we propose a reinforcement learning framework that trains agents to optimize utterances for persuasive impact. Through comprehensive experiments across three diverse SDGs, we demonstrate that our agents significantly outperform baselines. This work represents a significant step toward developing AI agents capable of strategic social influence, with implications extending to scenarios requiring persuasive communication. Our code and data are available at https://3dagentworld.github.io/leader_follower.

Hongjin Lin, Naveena Karusala, Chinasa T. Okolo et al.

Artificial Intelligence for Social Good (AI4SG) has emerged as a growing body of research and practice exploring the potential of AI technologies to tackle social issues. This area emphasizes interdisciplinary partnerships with community organizations, such as non-profits and government agencies. However, amidst excitement about new advances in AI and their potential impact, the needs, expectations, and aspirations of these community organizations--and whether they are being met--are not well understood. Understanding these factors is important to ensure that the considerable efforts by AI teams and community organizations can actually achieve the positive social impact they strive for. Drawing on the Data Feminism framework, we explored the perspectives of community organization members on their partnerships with AI teams through 16 semi-structured interviews. Our study highlights the pervasive influence of funding agendas and the optimism surrounding AI's potential. Despite the significant intellectual contributions and labor provided by community organization members, their goals were frequently sidelined in favor of other stakeholders, including AI teams. While many community organization members expected tangible project deployment, only two out of 14 projects we studied reached the deployment stage. However, community organization members sustained their belief in the potential of the projects, still seeing diminished goals as valuable. To enhance the efficacy of future collaborations, our participants shared their aspirations for success, calling for co-leadership starting from the early stages of projects. We propose data co-liberation as a grounding principle for approaching AI4SG moving forward, positing that community organizations' co-leadership is essential for fostering more effective, sustainable, and ethical development of AI.

AR Asriadi, M. Nasution, Sugianto Sugianto

The concept of property ownership in Islam has unique characteristics, which are in line with human nature. In contrast to the two currently developing concepts, capitalism and communism, neither system has succeeded in bringing individuals or groups into harmony with a social order of life. Individual property rights freedom is the foundation of the concept of capitalism, and the abolition of individual property rights is the main goal of socialism-communism teachings. Because humans are born into the world and do not own any property, the purpose of this paper is to investigate Islam's concept of ownership (the owner of the universe, the role of humans, and who provides everything). This article uses the literature study method, which entails gathering information from various sources, including books and related articles on property ownership in Islam. The findings of this study show that God owns everything in the universe, and humans did not have property in the world at first, but all the gifts that God gave with the guidance to use them as well as possible for the benefit of many people.

Hernán Camarero

Presentación

Aparna Ananthasubramaniam, Hong Chen, Jason Yan et al.

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

AAVV

Número completo

Hallvard Tjelmeland

Adrián Ascolani

A mediados del siglo XX, el Partido Comunista de la Argentina fue la fuerza política que procuró generar un movimiento de masas rurales, liderando las organizaciones sindicales y de productores agropecuarios, que actuara en un “frente democrático, antioligárquico y antiimperialista” con otros actores sociales para lograr una reforma agraria profunda y otros derechos específicos. En este artículo se reconstruye ese accionar en el período 1958-1962, vinculándolo con las políticas agrarias del gobierno de Arturo Frondizi, perjudiciales para estos sectores, empleando fuentes diversas generadas por los actores mencionados.

Ignacio Ibánez Cornet

Resumen de Elisa Pastoriza y Juan Carlos Torre, Mar del Plata, un sueño de los argentinos, Buenos Aires, Edhasa, 2019, 360 pp.

Leandro Sessa

Reseña de Martín Bergel, La desmesura revolucionaria. Cultura y política en los orígenes del APRA, Lima, La Siniestra Ensayos, 2019, 382 pp.

Shruti Phadke, Mattia Samory, Tanushree Mitra

Online discussion platforms offer a forum to strengthen and propagate belief in misinformed conspiracy theories. Yet, they also offer avenues for conspiracy theorists to express their doubts and experiences of cognitive dissonance. Such expressions of dissonance may shed light on who abandons misguided beliefs and under which circumstances. This paper characterizes self-disclosures of dissonance about QAnon, a conspiracy theory initiated by a mysterious leader Q and popularized by their followers, anons in conspiracy theory subreddits. To understand what dissonance and disbelief mean within conspiracy communities, we first characterize their social imaginaries, a broad understanding of how people collectively imagine their social existence. Focusing on 2K posts from two image boards, 4chan and 8chan, and 1.2 M comments and posts from 12 subreddits dedicated to QAnon, we adopt a mixed methods approach to uncover the symbolic language representing the movement, expectations, practices, heroes and foes of the QAnon community. We use these social imaginaries to create a computational framework for distinguishing belief and dissonance from general discussion about QAnon. Further, analyzing user engagement with QAnon conspiracy subreddits, we find that self-disclosures of dissonance correlate with a significant decrease in user contributions and ultimately with their departure from the community. We contribute a computational framework for identifying dissonance self-disclosures and measuring the changes in user engagement surrounding dissonance. Our work can provide insights into designing dissonance-based interventions that can potentially dissuade conspiracists from online conspiracy discussion communities.

Andrey Schelchkov

Este texto analiza la disidencia en el Partido Comunista chileno en los años 30, conocida como la división hidalguista. Surgida a causa de conflictos políticos internos, acompañada por una lenta intromisión del Secretariado Sudamericano de la Internacional Comunista, manifestó escasas divergencias con la doctrina partidaria. El artículo se basa en la documentación del Secretariado Internacional trotskista, lo que permite analizar sus relaciones con el trotskismo chileno, revelando la actitud ambigua del partido Izquierda Comunista al acatar las decisiones del Secretariado conservando su independencia política en el ámbito nacional.

Syed Afaq Ali Shah, Weifeng Deng, Jianxin Li et al.

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive social networks using images. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.

Jaber Valinejad, Lamine Mili

This paper develops a community resilience optimization method subject to power flow constraints in the Cyber-Physical-Social Systems in Power Engineering, which is solved using a multi-agent-based algorithm. The tool that makes the nexus between electricity generation on the physical side and the consumers and the critical loads on the social side is the power flow algorithm. Specifically, the levels of emotion, empathy, cooperation, and the physical health of the consumers, prosumers are modeled in the proposed community resilience optimization approach while accounting for the electric power system constraints and their impact on the critical loads, which include hospitals, shelters, and gas stations, to name a few. The optimization accounts for the fact that the level of satisfaction of the society, the living standards, and the social well-being are depended on the supply of energy, including electricity. Evidently, the lack of electric energy resulting from load shedding has an impact on both the mental and the psychical quality of life, which in turn affects the community resilience. The developed constrained community resilience optimization method is applied to two case studies, including a two-area 6-buses system and a modified IEEE RTS 24-bus system. Simulation results reveal that a decrease in the initial values of the emotion, the risk perception, and the social media platform effect factor entails an increase in load shedding, which in turn results in a decrease in community resilience. In contrast, an increase in the initial values of cooperation, empathy, physical health, the capacity of microgrids and distributed energy resources results in a decrease in the load shedding, which in turn induces an enhancement of the community resilience.