Hasil untuk "Socialism. Communism. Anarchism"

Moritz Pilarski, Nicolas Pröllochs

Fact-checking ecosystems on social media depend on the interplay between what users want checked and what contributors are willing to supply. Prior research has largely examined these forces in isolation, yet it remains unclear to what extent supply meets demand. We address this gap with an empirical analysis of a unique dataset of 1.1 million fact-checks and fact-checking requests from X's Community Notes platform between June 2024 and May 2025. We find that requests disproportionately target highly visible posts - those with more views and engagement and authored by influential accounts - whereas fact-checks are distributed more broadly across languages, sentiments, and topics. Using a quasi-experimental survival analysis, we further estimate the effect of displaying requests on subsequent note creation. Results show that requests significantly accelerate contributions from Top Writers. Altogether, our findings highlight a gap between the content that attracts requests for fact-checking and the content that ultimately receives fact-checks, while showing that user requests can steer contributors toward greater alignment. These insights carry important implications for platform governance and future research on online misinformation.

Rhea Basappa, Mustafa Tekman, Hong Lu et al.

Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness. We also report on its deployment in a live class.

Priyanshu Chaubey

Human communication has been profoundly changed by social media, which allows users to engage in previously unheard-of ways, such as text-based conversations, video chats, and live streaming. The digital landscape has started to change in recent years as a result of the introduction of Virtual Reality (VR) to these platforms. Instead of using conventional 2D screens, VR offers a completely immersive experience that lets users interact with content and one another in 3D spaces. This study examines the integration of virtual reality (VR) technology into social media applications, evaluating their potential to provide more dynamic and captivating digital spaces. Globally, social media sites like Facebook, Instagram, and Twitter have already changed the nature of communication. Immersion technologies like virtual reality (VR) represent the next stage, though, as they have the ability to change how we interact, connect, and share in social settings in addition to improving user experience.

Alessandro Bellina, Donald Ruggiero Lo Sardo, Emanuele Brugnoli et al.

Social media platforms have become essential spaces for public discourse. While political polarisation and limited communication across different groups are widely acknowledged, the connection between social network fragmentation and the language features and quality used by various communities has received insufficient attention. This study aims to fill this gap by examining the social structure and linguistic richness of the Italian debate on Twitter/X. We analyse tweets and retweets from Italian politicians and news outlets between 2018 and 2022, characterising the retweet network and evaluating the language used within different communities through various lexical metrics. Our analysis uncovers two systematic patterns: communities closer in the network tend to use more similar vocabulary, while isolated communities consistently demonstrate lower lexical diversity and richness. Together, these patterns illustrate what we call ``language bubbles''. These findings indicate that socially isolated communities interact less with others and develop distinct and poorer linguistic profiles, highlighting a structural link between social fragmentation and linguistic divergence.

Moein Shahiki Tash, Luis Ramos, Zahra Ahani et al.

The advent of social media has transformed communication, enabling individuals to share their experiences, seek support, and participate in diverse discussions. While extensive research has focused on identifying harmful content like hate speech, the recognition and promotion of positive and supportive interactions remain largely unexplored. This study proposes an innovative approach to detecting online social support in Spanish-language social media texts. We introduce the first annotated dataset specifically created for this task, comprising 3,189 YouTube comments classified as supportive or non-supportive. To address data imbalance, we employed GPT-4o to generate paraphrased comments and create a balanced dataset. We then evaluated social support classification using traditional machine learning models, deep learning architectures, and transformer-based models, including GPT-4o, but only on the unbalanced dataset. Subsequently, we utilized a transformer model to compare the performance between the balanced and unbalanced datasets. Our findings indicate that the balanced dataset yielded improved results for Task 2 (Individual and Group) and Task 3 (Nation, Other, LGBTQ, Black Community, Women, Religion), whereas GPT-4o performed best for Task 1 (Social Support and Non-Support). This study highlights the significance of fostering a supportive online environment and lays the groundwork for future research in automated social support detection.

Huiyun Tang, Björn Rohles, Yuwei Chuai et al.

Reducing the spread of misinformation is challenging. AI-based fact verification systems offer a promising solution by addressing the high costs and slow pace of traditional fact-checking. However, the problem of how to effectively communicate the results to users remains unsolved. Warning labels may seem an easy solution, but they fail to account for fuzzy misinformation that is not entirely fake. Additionally, users' limited attention spans and social media information should be taken into account while designing the presentation. The online experiment (n = 537) investigates the impact of sources and granularity on users' perception of information veracity and the system's usefulness and trustworthiness. Findings show that fine-grained indicators enhance nuanced opinions, information awareness, and the intention to use fact-checking systems. Source differences had minimal impact on opinions and perceptions, except for informativeness. Qualitative findings suggest the proposed indicators promote critical thinking. We discuss implications for designing concise, user-friendly AI fact-checking feedback.

Suwen Yang, Lei Shi

With the rapid development of information technology and the widespread utilization of recommendation algorithms, users are able to access information more conveniently, while the content they receive tends to be homogeneous. Homogeneous viewpoints and preferences tend to cluster users into sub-networks, leading to group polarization and increasing the likelihood of forming information cocoons. This paper aims to handle information cocoon phenomena in debates on social media. In order to investigate potential user connections, we construct a double-layer network that incorporates two dimensions: relational ties and feature-based similarity between users. Based on the structure of the multi-layer network, we promote two graph auto-encoder (GAE) based community detection algorithms, which can be applied to the partition and determination of information cocoons. This paper tests these two algorithms on Cora, Citeseer, and synthetic datasets, comparing them with existing multi-layer network unsupervised community detection algorithms. Numerical experiments illustrate that the algorithms proposed in this paper significantly improve prediction accuracy indicator NMI (normalized mutual information) and network topology indicator Q. Additionally, an influence-based intervention measure on which algorithms can operate is proposed. Through the Markov states transition model, we simulate the intervention effects, which illustrate that our community detection algorithms play a vital role in partitioning and determining information cocoons. Simultaneously, our intervention strategy alleviates the polarization of viewpoints and the formation of information cocoons with minimal intervention effort.

AAVV

Número completo

Florencia Gutiérrez

Reseña de Mario Barbosa Cruz y Miguel Ángel Gorostieta (eds.), Historias del trabajo y sus trabajadoras(es). Nuevos derroteros desde la historia social, Ciudad de México, CEMOS - CONAHCYT, 2024, 2024, 270 pgs.

Philip Baillargeon, Jina Yoon, Amy Zhang

Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from "being social" in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design.

Ujun Jeong, Bohan Jiang, Zhen Tan et al.

Decentralized social media platforms like Bluesky Social (Bluesky) have made it possible to publicly disclose some user behaviors with millisecond-level precision. Embracing Bluesky's principles of open-source and open-data, we present the first collection of the temporal dynamics of user-driven social interactions. BlueTempNet integrates multiple types of networks into a single multi-network, including user-to-user interactions (following and blocking users) and user-to-community interactions (creating and joining communities). Communities are user-formed groups in custom Feeds, where users subscribe to posts aligned with their interests. Following Bluesky's public data policy, we collect existing Bluesky Feeds, including the users who liked and generated these Feeds, and provide tools to gather users' social interactions within a date range. This data-collection strategy captures past user behaviors and supports the future data collection of user behavior.

Burleigh Hendrickson

Este artículo amplía las observaciones que hice en septiembre de 2023 en las IX Jornadas de Estudio y Reflexión sobre Movimientos Estudiantiles, y resume los puntos principales de mi reciente investigación plasmada en Decolonizing 1968: Transnational Student Activism in Tunis, Paris, and Dakar. Se sostiene que las protestas de 1968 deben ser entendidas como un momento postcolonial y explico mi comprensión de lo que significa “descolonizar” en 1968 en el mundo francófono frente a cómo la praxis descolonial ha sido articulada por académicos latinoamericanos clave. Por último, ofrezco viñetas de activistas que cruzaron fronteras como Omar Blondin-Diop, Daniel Cohn-Bendit y Michel Foucault.

Patricio Herrera

Reseña de Juan Carlos Yáñez Andrade, Los pobres están invitados a la mesa. La alimentación popular en Chile: 1930-1950, Santiago, RiL editores, 2023, 280 pgs.

Rodolfo Leyes

El movimiento obrero suele presentarse como un fenómeno de las grandes urbes. Sin embargo, la concentración urbana y en torno a la industria, no constituyeron las condiciones objetivas de miles de trabajadores argentinos. Para estudiar cómo se vencieron los desafíos de un medio rural, reconstruiremos la experiencia entrerriana. Una provincia con una gran dispersión demográfica y sin una ciudad que hegemonice la vida social se presenta como un espacio propicio para analizar la evolución del movimiento obrero. El recorte abarca desde la primera huelga general hasta la crisis de la Unión Obrera Provincial de Entre Ríos en 1937.

Andrea Sol Franco, María Josefina Duarte, Carlos Álvarez

La consolidación del proceso de acumulación capitalista encontró en Rosario y Santa Fe polos de atracción privilegiados por sus dinámicas demográficas y productivas. Allí, la clase trabajadora fue modelando formas organizacionales, de ocupación y apropiación, tanto material y simbólica, del espacio público en interacción y resistencia con los modelos productivos y urbanísticos impulsados por las élites. A partir de diversas fuentes daremos cuenta de manera comparativa y relacional las implicancias de la construcción y apropiación de una espacialidad en vinculación con los procesos identitarios de la clase trabajadora santafesina y rosarina de inicios del siglo XX.

Caroline Gora, Ana Dudas, Lucas Court et al.

Autism spectrum disorders are complex, polygenic and heterogenous neurodevelopmental conditions, imposing a substantial economic burden. Genetics are influenced by the environment, specifically the social experience during the critical neurodevelopmental period. Despite efficacy of early behavior interventions targeted specific behaviors in some autistic children, there is no sustainable treatment for the two core symptoms: deficits in social interaction and communication, and stereotyped or restrained behaviors or interests. In this study, we investigated the impact of the social environment on both wild-type (WT) and Shank3 knockout (KO) mice, a mouse model that reproduces core autism-like symptoms. Our findings revealed that WT mice raised in an enriched social environment maintained social interest towards new conspecifics across multiple trials. Additionally, we observed that 2 hours or chronic social isolation induced social deficits or enhanced social interaction and olfactory neuron responses in WT animals, respectively. Notably, chronic social isolation restored both social novelty and olfactory deficits, and normalized self-grooming behavior in Shank3 KO mice. These results novel insights for the implementation of behavioral intervention and inclusive classrooms programs for children with ASD.

Max Hashem Eiza, Vinh Thong Ta

The increasing importance and consequent challenges of establishing indirect trusted relationships in highly dynamic social networks such as vehicular social networks (VSNs), are investigated in this paper. VSNs are mobile social networks that aim to create social links among travellers on the roads. Besides matching interests between two users, social trust is essential to successfully establish and nurture a social relationship. However, the unique characteristics of VSNs pose many challenges such as uncertainty, subjectivity and intransitivity to indirect social trust modelling. Furthermore, the current trust models in the literature inadequately address trust propagation in VSNs. We propose a novel indirect social trust model for VSNs using evolving graph theory and the Paillier cryptosystem. We consider the VSN as a highly dynamic social evolving graph where social ties among vehicles hold a trustworthiness factor that evolves over time. This factor is estimated based on the behaviours, opinions, distances, and communication metrics of the parties involved. Employing the homomorphic property of the Paillier cryptosystem, the proposed model targets the subjectivity problem when combining multiple opinions to establish an indirect trusted relationship. Through analysis of computational and communication complexities, we show the viability of the proposed model and the efficiency of its indirect trust computation algorithm.

María Fernanda Alle, Laura Prado Acosta

Presentación del dossier

M. Quirico, G. Ragona

Samuele Bolotta, Guillaume Dumas

This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We argue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the dark matter of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.