Modelling the complex dynamics of online social platforms is critical for addressing challenges such as hate speech and misinformation. While Discussion Transformers, which model conversations as graph structures, have emerged as a promising architecture, their potential is severely constrained by reliance on high-quality, human-labelled datasets. In this paper, we advocate a paradigm shift from task-specific fine-tuning to unsupervised pretraining, grounded in an entirely novel consideration of community norms. We posit that this framework not only mitigates data scarcity but also enables interpretation of the social norms underlying the decisions made by such an AI system. Ultimately, we believe that this direction offers many opportunities for AI for Social Good.
Online support communities have become vital spaces offering varied forms of support to individuals facing mental health challenges. Despite the proliferation of platforms with distinct technical structures, little is known about how these features shape support dynamics and the socio-technical mechanisms at play. This study introduces a technical-structural-functional model of social support and systematically compares communication network structures and support types in 20 forum-based and 20 chat-based mental health communities. Using supervised machine learning and social network analysis, we find that forum-based communities foster more informational and emotional support, whereas chat-based communities promote greater companionship. These patterns were partially explained by network structure: higher in-degree centralization in forums accounted for the prevalence of informational support, while decentralized reply patterns in chat groups accounted for more companionship. These findings extend the structural-functional model of support to online contexts and provide actionable guidance for designing support communities that align technical structures with users' support needs.
Humans live and act in 3D space, but often work and communicate on 2D surfaces. The prevalence of online communication on 2D screens raises the issue of whether human spatial configuration affects our capabilities, social perception, and behaviors when interacting with others in 2D video chat. How do factors like location, setting, and context subtly shape our online communication, particularly in scenarios such as social support and topic-based discussions? Using Ohyay.co as a platform, we compared a normal gallery interface with a scene-based Room-type interface where participants are located in circular arrangement on screen in a social support task, and found that participants allocated attention to the group as a whole, and had pronounced self-awareness in the Room format. We then chose a two-sided topic for discussion in the Gallery interface and the Room interface where participants on each team face-off against each other, and found that they utilized spatial references to orient their allegiances, expressing greater engagement with those farther away in digital space and greater empathy with those closer, in the Room over the Gallery format. We found spatial effects in the way participants hide from the spotlight, in perspective-taking, and in their use of expressive gestures in time on the screen. This work highlights the need for considering spatial configuration in 2D in the design of collaborative communication systems to optimize for psychological needs for particular tasks.
Este artículo recoge los posicionamientos mediante los cuales el Partido Comunista de la Argentina comenzó a desandar la política de convergencia cívico-militar cuando la última dictadura atravesaba un momento crítico. En estas páginas, se sostiene que, en el marco de la línea de Convenio Nacional Democrático, el PCA pujó por ser reconocido como un actor de peso en el concierto de los partidos políticos burgueses, lo cual orientó su línea política. La propuesta de este trabajo es indagar desde una mirada cupular las modulaciones en el discurso público y explorar las tentativas fallidas de ingresar a la Multipartidaria.
1789-, Labor in politics. Political activity of the working class
Stiftelseskongressen til Den andre internasjonalen som ble holdt i Paris i 1889, markerte ikke bare hundreårsdagen for stormingen av Bastillen, men også selve gjenfødelsen av den internasjonale sosialistbevegelsen. Det oppsto raskt konflikter mellom de franske revolusjonære marxistene og de reformistiske possibilistene som begge holdt konkurrerende internasjonale kongresser i Paris. Det norske Arbeiderparti tok et radikalt standpunkt mot foreningen av de to kongressene og stilte seg på linje med de mest revolusjonære delene av den internasjonale sosialistbevegelsen. Nordmennenes innledende radikale holdning overfor franske reformister endret seg drastisk ved den kontroversielle sosialisten Alexandre Millerands inntreden i den borgerlige Waldeck-Rousseau-regjeringen i 1899. Med denne artikkelen ønsker jeg å undersøke Det norske Arbeiderpartis perspektiv på den franske sosialistiske bevegelsen i tiden før 1914, særlig spørsmålet om sekterisme i den franske bevegelsen og sosialistenes koalisjon med borgerlige partier.
Socialism. Communism. Anarchism, Economic history and conditions
Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling interactions in online communities. We propose a method for jointly modelling community structure and language over time. Our system produces dynamic word and user representations that can be used to cluster users, investigate thematic interests of groups, and predict group membership. We apply and evaluate our method in the context of a set of misogynistic extremist groups. Our results indicate that this approach outperforms prior models which lacked one of these components (i.e. not incorporating social structure, or using static word embeddings) when evaluated on clustering and embedding prediction tasks. Our method further enables novel types of analyses on online groups, including tracing their response to temporal events and quantifying their propensity for using violent language, which is of particular importance in the context of extremist groups.
Federico Albanese, Esteban Feuerstein, Pablo Balenzuela
Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these patterns. In this study, we examine the community structure of re-post networks within the context of a polarized political environment at two different times. We specifically identify individuals who consistently switch between opposing communities and analyze the key features that distinguish them. Our investigation focuses on two crucial aspects of these users: the topological properties of their interactions and the political bias in the content of their posts. Our analysis is based on a dataset comprising 2 million tweets related to US President Donald Trump, coupled with data from over 100 000 individual user accounts spanning the 2020 US presidential election year. Our findings indicate that individuals who switch communities exhibit disparities compared to those who remain within the same communities, both in terms of the topological aspects of their interaction patterns (pagerank, degree, betweenness centrality.) and in the sentiment bias of their content towards Donald Trump.
We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent's emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence.
The large scale usage of social media, combined with its significant impact, has made it increasingly important to understand it. In particular, identifying user communities, can be helpful for many downstream tasks. However, particularly when models are trained on past data and tested on future, doing this is difficult. In this paper, we hypothesize to take advantage of Large Language Models (LLMs), to better identify user communities. Due to the fact that many LLMs, such as ChatGPT, are fixed and must be treated as black-boxes, we propose an approach to better prompt them, by training a smaller LLM to do this. We devise strategies to train this smaller model, showing how it can improve the larger LLMs ability to detect communities. Experimental results show improvements on Reddit and Twitter data, on the tasks of community detection, bot detection, and news media profiling.
Autism Spectrum Disorder (ASD) is a lifelong condition that significantly influencing an individual's communication abilities and their social interactions. Early diagnosis and intervention are critical due to the profound impact of ASD's characteristic behaviors on foundational developmental stages. However, limitations of standardized diagnostic tools necessitate the development of objective and precise diagnostic methodologies. This paper proposes an end-to-end framework for automatically predicting the social communication severity of children with ASD from raw speech data. This framework incorporates an automatic speech recognition model, fine-tuned with speech data from children with ASD, followed by the application of fine-tuned pre-trained language models to generate a final prediction score. Achieving a Pearson Correlation Coefficient of 0.6566 with human-rated scores, the proposed method showcases its potential as an accessible and objective tool for the assessment of ASD.
Este trabajo se propone analizar, desde una dimensión transnacional, un tópico particular en relación con la sexualidad y el amor libre presente en el movimiento anarquista: el problema de los celos. Para ello, se focaliza en el estudio de las intertextualidades entre distintxs referentes del anarquismo, haciendo
hincapié en la circulación de ideas y conexiones desde y hacia Argentina. La prensa vinculada con el anarquismo individualista y, particularmente, una obra del médico anarquista Juan Lazarte sobre los celos, conforman el corpus documental que nos permite evidenciar cómo la crítica al monopolio sexual no estuvo desvinculada de la lucha contra el Estado y el capital.
1789-, Labor in politics. Political activity of the working class
Individuals involved in gang-related activity use mainstream social media including Facebook and Twitter to express taunts and threats as well as grief and memorializing. However, identifying the impact of gang-related activity in order to serve community member needs through social media sources has a unique set of challenges. This includes the difficulty of ethically identifying training data of individuals impacted by gang activity and the need to account for a non-standard language style commonly used in the tweets from these individuals. Our study provides evidence of methods where natural language processing tools can be helpful in efficiently identifying individuals who may be in need of community care resources such as counselors, conflict mediators, or academic/professional training programs. We demonstrate that our binary logistic classifier outperforms baseline standards in identifying individuals impacted by gang-related violence using a sample of gang-related tweets associated with Chicago. We ultimately found that the language of a tweet is highly relevant and that uses of ``big data'' methods or machine learning models need to better understand how language impacts the model's performance and how it discriminates among populations.
The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.
This research examines the propagation of rumors on social networks during public health emergencies and explores strategies to effectively manage false information in cyberspace. Using a simulation model, the study analyzes the impact of factors such as communication channel control, government intervention, and individual personalities on the spread of rumors. The results suggest that enhancing netizens' knowledge and capacity to recognize and resist rumors, developing rumor-debunking platforms, and promoting a "clear" ecology of network information content are effective strategies for controlling false information in cyberspace. However, the complexity and scale of actual networks present challenges to the development of a comprehensive cyberspace governance system. The findings offer practical guidelines for improving the effectiveness of governance in managing the spread of rumors on social networks.
This article discusses how Sarekat Islam (SI) became a representation of the social movements of the indigenous people in the Dutch East Indies. This research uses the historical method with a sociological approach. Furthermore, this study uses resource mobilization theory in social movements to see the development of thought patterns and movements in SI. The results of this study indicate that, at first, for the indigenous people of SI, it was considered a manifestation of the Ratu Adil movement, where the leaders were considered to have charismatic personalities who would lead them to escape the misery of life. However, this assumption later changed, not only because the leaders of the SI rejected Ratu Adil's assumption but also because the SI itself began to develop a more rational and modern thought, namely socialism and Islamic reformism, in response to conditions in the Dutch East Indies, which eventually realizing the development of ideology and movement within the SI. Socialism-Marxism (communism), which is fundamentally contrary to Islam, made SI eventually split up. Many conflicts occur between the camp that adheres to communism with the anti-communism camp. The peak was when SI began to expressly rid itself of elements of communism through party discipline in 1921.Artikel ini membahas bagaimana Sarekat Islam (SI) menjadi representasi dari gerakan sosial rakyat pribumi di Hindia Belanda. Penelitian ini mengunakan metode sejarah dengan pendekatan sosiologi. Lebih lanjut penelitian ini menggunakan teori mobilisasi sumber daya dalam gerakan sosial untuk melihat perkembangan pola pemikiran dan gerakan di SI. Hasil Penelitian ini menunjukkan bahwa pada awalnya, bagi rakyat pribumi SI dianggap sebagai wujud dari gerakan Ratu Adil dimana para pemimpinnya dianggap memiliki kharismatik yang akan memimpin mereka agar lepas dari kesengsaraan hidup. Namun anggapan ini kemudian berubah, bukan hanya karena para pemimpin SI menolak anggapan Ratu Adil itu, namun juga karena di dalam Sarekat Islam sendiri mulai berkembang sebuah pemikiran yang lebih rasional dan modern yakni sosialisme serta reformisme Islam dalam merespon kondisi di Hindia Belanda, yang akhirnya mewujudkan perkembangan ideologi dan gerakan di dalam SI. Paham sosialisme-marxisme (komunisme) yang bertentangan dasar dengan Islam membuat SI akhirnya terpecah-belah. Banyak konflik terjadi antara kubu yang menganut komunisme dengan kubu yang anti komunisme. Puncaknya yakni ketika SI mulai secara tegas membersihkan diri dari unsur komunisme melalui disiplin partai pada 1921.
Resumen de Velia Luparello, Los trotskistas bajo el terror nazi. Una historia de la IV Internacional durante la Segunda Guerra Mundial, Santiago de Chile, Ariadna, 2021, 373 pgs.
1789-, Labor in politics. Political activity of the working class
El objetivo de este trabajo es analizar el proceso de inserción en el campo intelectual de Luis Franco, prestando atención a sus ámbitos de formación y a su participación en algunas revistas literarias y culturales. También
nos abocaremos a pensar cómo fue el acercamiento de este escritor al mundo de la política, atendiendo a cómo este cruce se plasmó en su obra y analizando sus reflexiones en torno a las responsabilidades del intelectual y a las funciones del arte.
1789-, Labor in politics. Political activity of the working class