Este artículo analiza cómo, entre 1919 y 1955, Félix Weil atravesó tres etapas en su pensamiento y en su acción política. Su formación revolucionaria se inició junto a los soldados y obreros en Frankfurt. Tras sus textos iniciales sobre la socialización, se inclinó por la historia inmediata y los problemas de los precios en una economía natural sin mercado. En 1930 se volcó hacia la formulación de sistemas tributarios igualitarios según el ingreso y a las teorías de las economías dirigidas. Desde 1940 y 1950 se involucró en los debates del período de formación del peronismo que se inscribieron en una polémica aún más amplia en torno de la democracia, el autoritarismo y el totalitarismo.
1789-, Labor in politics. Political activity of the working class
Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains regarding the role of technology in mediating and enabling communication within today's epistemic communities. This paper addresses this gap by examining how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of researchers from minority groups. Using agent-based simulations, we investigate this question with respect to components of a widely used recommendation algorithm, and uncover three key patterns: First, these algorithms disproportionately harm the professional visibility of researchers from minority groups, creating systemic patterns of exclusion. Second, within these minority groups, the algorithms result in greater visibility for users who more closely resemble the majority group, incentivizing assimilation at the cost of professional invisibility. Third, even for topics that strongly align with minority identities, content created by minority researchers is less visible to the majority than similar content produced by majority users. Importantly, these patterns emerge, even though individual engagement with professional content is independent of group identity. These findings have significant implications for philosophical discussions on epistemic injustice and exclusion, and for policy proposals aimed at addressing these harms. More broadly, they call for a closer examination of the pervasive, but often neglected role of AI and data-driven technologies in shaping today's epistemic communities.
The advent of Large Language Models (LLMs) and Artificial Intelligence (AI) tools has revolutionized various facets of our lives, particularly in the realm of social media. For students, these advancements have unlocked unprecedented opportunities for learning, collaboration, and personal growth. AI-driven applications are transforming how students interact with social media, offering personalized content and recommendations, and enabling smarter, more efficient communication. Recent studies utilizing data from UniversityCube underscore the profound impact of AI tools on students' academic and social experiences. These studies reveal that students engaging with AI-enhanced social media platforms report higher academic performance, enhanced critical thinking skills, and increased engagement in collaborative projects. Moreover, AI tools assist in filtering out distracting content, allowing students to concentrate more on educational materials and pertinent discussions. The integration of LLMs in social media has further facilitated improved peer-to-peer communication and mentorship opportunities. AI algorithms effectively match students based on shared academic interests and career goals, fostering a supportive and intellectually stimulating online community, thereby contributing to increased student satisfaction and retention rates. In this article, we delve into the data provided by UniversityCube to explore how LLMs and AI tools are specifically transforming social media for students. Through case studies and statistical analyses, we offer a comprehensive understanding of the educational and social benefits these technologies offer. Our exploration highlights the potential of AI-driven tools to create a more enriched, efficient, and supportive educational environment for students in the digital age.
The Partnership for Integration of Computation into Undergraduate Physics (PICUP) was founded in the mid-2010s to assist educators with the challenges of integrating computation into physics curricula. In addition to in-person trainings and hosted educational materials, PICUP uses a Slack Workspace to continue collaboration and discussion offline. In this work, we use Social Network Analysis (SNA) to study the communication patterns of PICUP and assess if PICUP is meeting their goals in the Slack environment. Through our analysis, we discuss PICUP's community structure and define a conceptual framework to evaluate if the goals are being met through SNA metrics. We present a comprehensive analysis of eight channels in the Slack Workspace using various SNA metrics, identifying three distinct levels of user engagement. We conclude with implications for PICUP and provide recommendations for the community.
Online mental health communities (OMHCs) offer rich posts and comments for viewers, who do not directly participate in the communications, to seek social support from others' experience. However, viewers could face challenges in finding helpful posts and comments and digesting the content to get needed support, as revealed in our formative study (N=10). In this work, we present an interactive visual tool named ComViewer to help viewers seek social support in OMHCs. With ComViewer, viewers can filter posts of different topics and find supportive comments via a zoomable circle packing visual component that adapts to searched keywords. Powered by LLM, ComViewer supports an interactive sensemaking process by enabling viewers to interactively highlight, summarize, and question any community content. A within-subjects study (N=20) demonstrates ComViewer's strengths in providing viewers with a more simplified, more fruitful, and more engaging support-seeking experience compared to a baseline OMHC interface without ComViewer. We further discuss design implications for facilitating information-seeking and sense making in online mental health communities.
Carolina Luque, Isabella Agudelo, Kevin Leal
et al.
Statistical analysis of social networks is a predominant methodology in political science research. In this article we implement network methods to characterize the presidential inauguration speech and identify political communities in Colombia. We propose an empirical approach to analize the discursive structure of the heads of state and the configuration of alliance and work relationships between prominent figures of Colombian politics. Thus, we implement network methods from two perspectives, words and political actors. We conclude on the relevance of social network statistics to identify frequent and important terms in the communicative action of president figures, and to examine cohesion such discourses. Finally, we distinguish notable actors in the consolidation of working relationships and alliances as well as political communities.
Richard Alvarez, Paras Bhatt, Xingmeng Zhao
et al.
Who actually expresses an intent to buy GameStop shares on Reddit? What convinces people to buy stocks? Are people convinced to support a coordinated plan to adversely impact Wall Street investors? Existing literature on understanding intent has mainly relied on surveys and self reporting; however there are limitations to these methodologies. Hence, in this paper, we develop an annotated dataset of communications centered on the GameStop phenomenon to analyze the subscriber intentions behaviors within the r/WallStreetBets community to buy (or not buy) stocks. Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop. Overall, our dataset can provide insight to social scientists on the persuasive power to buy into social movements online by adopting common language and narrative. WARNING: This paper contains offensive language that commonly appears on Reddit's r/WallStreetBets subreddit.
Mario Montagud, Gianluca Cernigliaro, Miguel Arevalillo-Herráez
et al.
Technological advances can bring many benefits to our daily lives, and this includes the education and training sectors. In the last years, online education, teaching and training models are becoming increasingly adopted, in part influenced by major circumstances like the pandemic. The use of videoconferencing tools in such sectors has become fundamental, but recent research has shown their multiple limitations in terms of relevant aspects, like comfort, interaction quality, situational awareness, (co-)presence, etc. This study elaborates on a new communication, interaction and collaboration medium that becomes a promising candidate to overcome such limitations, by adopting immersive technologies: Social Virtual Reality (VR). First, this article provides a comprehensive review of studies having provided initial evidence on (potential) benefits provided by Social VR in relevant use cases related to education, such as online classes, training and co-design activities, virtual conferences and interactive visits to virtual spaces, many of them including comparisons with classical tools like 2D conferencing. Likewise, the potential benefits of integrating realistic and volumetric users' representations to enable multi-party holographic communications in Social VR is also discussed. Next, this article identifies and elaborates on key limitations of existing studies in this field, including both technological and methodological aspects. Finally, it discusses key remaining challenges to be addressed to fully exploit the potential of Social VR in the education sector.
Communication is a hallmark of intelligence. In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in assistive shared-control settings. MIRROR is inspired by social projection theory, which hypothesizes that humans use self-models to understand others. Likewise, MIRROR leverages self-models learned using reinforcement learning to bootstrap human modeling. Experiments with simulated humans show that this approach leads to rapid learning and more robust models compared to existing behavioral cloning and state-of-the-art imitation learning methods. We also present a human-subject study using the CARLA simulator which shows that (i) MIRROR is able to scale to complex domains with high-dimensional observations and complicated world physics and (ii) provides effective assistive communication that enabled participants to drive more safely in adverse weather conditions.
Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand, emerge, and extinct. However, existing pattern-based approaches are limited in supporting quantitative analysis, and are potentially problematic for using the patterns in a mutually exclusive manner when describing community evolution. In this work, we propose that different patterns can occur simultaneously between every pair of communities during the evolution, just in different degrees. Four entropy-based indices are devised to measure the degree of community split, shrink, merge, and expand, respectively, which can provide a comprehensive and quantitative measure of community evolution in DSNs. The indices have properties desirable to quantify community evolution including monotonicity, and bounded maximum and minimum values that correspond to meaningful cases. They can also be combined to describe more patterns such as community emerge and extinct. We conduct experiments with real-world OSS projects to evaluate the validity of the proposed indices. The results suggest the proposed indices can effectively capture community evolution, and are consistent with existing approaches in detecting evolution patterns in DSNs with an accuracy of 94.1\%. The results also show that the indices are useful in predicting OSS team productivity with an accuracy of 0.718. In summary, the proposed approach is among the first to quantify the degree of community evolution with respect to different patterns, which is promising in supporting future research and applications about DSNs and OSS development.
Kristina Hristakieva, Stefano Cresci, Giovanni Da San Martino
et al.
Large-scale manipulations on social media have two important characteristics: (i) use of propaganda to influence others, and (ii) adoption of coordinated behavior to spread it and to amplify its impact. Despite the connection between them, these two characteristics have so far been considered in isolation. Here we aim to bridge this gap. In particular, we analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. We first propose and evaluate several metrics for measuring the use of propaganda on Twitter. Then, we investigate the use of propaganda by different coordinated communities that participated in the online debate. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities. Finally, we compare our measures of propaganda and coordination with automation (i.e., bot) scores and Twitter suspensions, revealing interesting trends. From a theoretical viewpoint, we introduce a methodology for analyzing several important dimensions of online behavior that are seldom conjointly considered. From a practical viewpoint, we provide new insights into authentic and inauthentic online activities during the 2019 UK general election.
El artículo presenta un panorama de las lecturas, relecturas y reinterpretaciones de la obra de Jean-Paul Sartre en la Argentina, en las décadas del 60 y 70, enfocándose particularmente en las implicancias políticas de su obra leída desde un país periférico. Tras un primer momento de acercamiento a Sartre por los miembros de la revista Contorno, los años 60 observan la confluencia y superposición de ideas sartreanas con el primer estructuralismo.
1789-, Labor in politics. Political activity of the working class
Rediet Abebe, Salvatore Giorgi, Anna Tedijanto
et al.
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation. Extensive public health research is ongoing to better understand the characteristics of communities with relatively high or low rates. In this work, we explore the role that social media language can play in providing insights into such community characteristics. Analyzing pregnancy-related tweets generated in US counties, we reveal a diverse set of latent topics including Morning Sickness, Celebrity Pregnancies, and Abortion Rights. We find that rates of mentioning these topics on Twitter predicts maternal mortality rates with higher accuracy than standard socioeconomic and risk variables such as income, race, and access to health-care, holding even after reducing the analysis to six topics chosen for their interpretability and connections to known risk factors. We then investigate psychological dimensions of community language, finding the use of less trustful, more stressed, and more negative affective language is significantly associated with higher mortality rates, while trust and negative affect also explain a significant portion of racial disparities in maternal mortality. We discuss the potential for these insights to inform actionable health interventions at the community-level.
We consider the problem of robust multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents, mainly those trained in a centralized way, can be brittle because they can adopt policies that act under the expectation that other agents will act a certain way rather than react to their actions. Our objective is to bias the learning process towards finding strategies that remain reactive towards others' behavior. Social empowerment measures the potential influence between agents' actions. We propose it as an additional reward term, so agents better adapt to other agents' actions. We show that the proposed method results in obtaining higher rewards faster and a higher success rate in three cooperative communication and coordination tasks.
The truss, a relaxation of the clique based on triangles, serves to identify clusters of actors in a way that is easy to interpret and is computationally attractive. This paper introduces the 4-cycle-based relative to the truss, called the trapeze, presents a weighted extension of both the truss and trapeze, and offers the refinements of strong trusses and trapezes and summit trusses and trapezes. Use of trapezes permits the application to bipartite graphs, while the weighted versions permit variation of support due to natural edge weights without increasing computational complexity. Finally, strong and summit versions make for easy determination of communities across graphs of varying density. Each of these structures offers guaranteed computation in polynomial time, is motivated by a natural observation of social cohesion, and is related nicely to other standard structures.
Los días 3, 4 y 5 de octubre se realizaron en Buenos Aires las “II Jornadas Internacionales de historia del movimiento obrero y la izquierda” organizadas por la revista Archivos y el CEHTI. El panel de cierre del evento se dedicó a un examen de la evolución, el actual estado y las perspectivas de nuestro campo de estudios, bajo el título que preside esta Sección. La mesa estuvo integrada por el Dr. Sergio Grez Toso, profesor e investigador de la Universidad de Chile; la Dra. Gabriela Águila, profesora e investigadora del Conicet y la Universidad Nacional de Rosario, y el Dr. Hernán Camarero, profesor e investigador del Conicet y la Universidad de Buenos Aires y director del CEHTI. A continuación se transcriben las intervenciones, revisadas por sus autores.
1789-, Labor in politics. Political activity of the working class