Tom Bidewell, Artemis Deligianni, Tuğrulcan Elmas
et al.
The influence of gender on online political communication remains contested, with existing scholarship providing mixed evidence as to whether gender shapes political messaging in digital environments. However, this debate has largely centred on mainstream platforms such as X (formerly Twitter), leaving the dynamics of alt-tech social media underexamined. This paper addresses this gap by analysing gendered patterns of political communication on Truth Social, a hyper-partisan platform that functions as a hub for the most committed followers of the American far right, a community closely associated with hegemonic masculine norms. To address this gap, we present the first large-scale analysis of political elite communication on Truth Social, using a novel dataset of 107k posts from 129 U.S. political figures. We examine the extent to which gender influences rhetorical style, topic framing, and audience engagement. We find that many gendered communication patterns documented on mainstream platforms persist on Truth Social. In particular, women political elites tend to express more joy and less anger than men and receive significantly higher levels of audience engagement. At the same time, more nuanced differences emerge. Although men and women political elites discuss largely similar conservative themes, they differ in how these issues are framed and in the rhetorical strategies employed. Notably, posts associated with women political elites contain higher levels of fear-based rhetoric, potentially suggesting selective adaptation in communicative style to navigate gender norms on the platform. These findings suggest that on Truth Social, an alt-tech platform with distinct ideological characteristics, mainstream gendered constraints persist, but are expressed through platform-specific communicative patterns shaped by its partisan orientation and sociotechnical environment.
Este artículo describe el surgimiento del Movimiento de Izquierda Revolucionaria de Perú, desde sus inicios como una pequeña escisión del APRA hasta su derrota militar en 1965. Trata el contexto en el cual surgió, con énfasis en el movimiento campesino, sus concepciones teóricas y la preparación y desarrollo de la lucha guerrillera. Argumentamos que la organización estaba concebida para iniciar la lucha armada en Perú. Para este trabajo usamos fuentes bibliográficas y entrevistas con ex militantes de la organización. Concluimos que la preconcepción de iniciar la lucha guerrillera fue precisamente uno de los factores que causaron su fracaso militar.
1789-, Labor in politics. Political activity of the working class
Sohyeon Hwang, Sophie Rollins, Thatiany Andrade Nunes
et al.
Decentralizing the governance of social computing systems to communities promises to empower them to make independent decisions, with nuance and in accordance with their values. Yet, communities do not govern in isolation. Many problems communities face are common, or move across their boundaries. We therefore propose designing for "inter-community governance:" mechanisms that support relationships and interactions between communities to coordinate on governance issues. Drawing from workshops with 24 individuals on decentralized, community-run social media, we present six challenges in designing for inter-community governance surfaced through ideas proposed in workshops. Together, these ideas come together as an ecosystem of resources, infrastructures, and tools that highlight three key principles for designing for inter-community governance: modularity, forkability, and polycentricity. We end with a discussion of how the ideas proposed in workshops might be implemented in future work aiming to support community governance in social computing systems broadly.
Yining Zhao, Sourav S Bhowmick, Nastassja L. Fischer
et al.
Recently, numerous community search methods for large graphs have been proposed, at the core of which is defining and measuring cohesion. This paper experimentally evaluates the effectiveness of these community search algorithms w.r.t. cohesiveness in the context of online social networks. Social communities are formed and developed under the influence of group cohesion theory, which has been extensively studied in social psychology. However, current generic methods typically measure cohesiveness using structural or attribute-based approaches and overlook domain-specific concepts such as group cohesion. We introduce five novel psychology-informed cohesiveness measures, based on the concept of group cohesion from social psychology, and propose a novel framework called CHASE for evaluating eight representative community search algorithms w.r.t. these measures on online social networks. Our analysis reveals that there is no clear correlation between structural and psychological cohesiveness, and no algorithm effectively identifies psychologically cohesive communities in online social networks. This study provides new insights that could guide the development of future community search methods.
This paper introduces SCRAG, a prediction framework inspired by social computing, designed to forecast community responses to real or hypothetical social media posts. SCRAG can be used by public relations specialists (e.g., to craft messaging in ways that avoid unintended misinterpretations) or public figures and influencers (e.g., to anticipate social responses), among other applications related to public sentiment prediction, crisis management, and social what-if analysis. While large language models (LLMs) have achieved remarkable success in generating coherent and contextually rich text, their reliance on static training data and susceptibility to hallucinations limit their effectiveness at response forecasting in dynamic social media environments. SCRAG overcomes these challenges by integrating LLMs with a Retrieval-Augmented Generation (RAG) technique rooted in social computing. Specifically, our framework retrieves (i) historical responses from the target community to capture their ideological, semantic, and emotional makeup, and (ii) external knowledge from sources such as news articles to inject time-sensitive context. This information is then jointly used to forecast the responses of the target community to new posts or narratives. Extensive experiments across six scenarios on the X platform (formerly Twitter), tested with various embedding models and LLMs, demonstrate over 10% improvements on average in key evaluation metrics. A concrete example further shows its effectiveness in capturing diverse ideologies and nuances. Our work provides a social computing tool for applications where accurate and concrete insights into community responses are crucial.
We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration was the opinion confrontation between capitalism, imperialism (blue opinion) and socialism, communism (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We found that the global network opinion favors socialism, communism for all six editions. The model also determined the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between Christianity and Islam, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks.
As an important fundamental task of numerous real-world applications such as social network analysis and online advertising/marketing, several prior works studied influential community search, which retrieves a community with high structural cohesiveness and maximum influences on other users in social networks. However, previous works usually considered the influences of the community on arbitrary users in social networks, rather than specific groups (e.g., customer groups, or senior communities). Inspired by this, we propose a novel Top-M Reverse Influential Community Search (TopM-RICS) problem, which obtains a seed community with the maximum influence on a user-specified target community, satisfying both structural and keyword constraints. To efficiently tackle the TopM-RICS problem, we design effective pruning strategies to filter out false alarms of candidate seed communities, and propose an effective index mechanism to facilitate the community retrieval. We also formulate and tackle a TopM-RICS variant, named Top-M Relaxed Reverse Influential Community Search} (TopM-R2ICS), which returns top-M subgraphs with relaxed structural constraints and having the maximum influence on a user-specified target community. Comprehensive experiments have been conducted to verify the efficiency and effectiveness of our TopM-RICS and TopM-R2ICS approaches on both real-world and synthetic social networks under various parameter settings.
ABSTRACT During the 2010s, several studies questioned the conceptual stability of “post-socialism.” I review some of these critiques, paying particular attention to the contradictions of the “Global East,” proposed lately as a substitute concept. Resisting this recent call to abandon it and building on recent scholarship, I suggest three spatial and temporal extensions that will assist researchers in using the lens of “post-socialism” to explore new questions, by: documenting the use of “strategic anachronisms,” best illustrated by universal anti-communism; configuring post-socialism as a heterochrony and messy temporality; and reorienting the concept from its connection with the past to a connection with a potential socialist future, among other things by provincializing East European/Former Soviet Union post-socialisms. The concept of post-socialism prompts plenty of inspiring questions about theory-making, challenges to neoliberal normalization, shifting notions of value, failure and success, local and global histories and possible futures for socialism, and comparative urbanism, thus allowing further exploration of contemporary and emerging politics, East and West, South and North. Scholars might be better off leaving the concept radically open and using it, selectively, as a toolkit, without relying on deterministic assumptions and putative master narratives. “To let a thousand flowers bloom” might be a more productive epistemological terrain than attempting to rationalize and then eradicate the concept of post-socialism. Assigning meta categories that classify global space is, of course, optional; if we have to give a name to our object of study, scholars should use, for want of a better term, post-socialism.
At the beginning of the XIX century, the world's people entered a new phase of life. But this era gave birth to a more complex and "expensive" social face. The industry is a main form that inundates the community's yard. The spirit of individualism creates a quasi-binary opposition. Strong-weak, rich-poor. One by one, various ideologies emerged as the antithesis of Capitalism, from socialism-communism to democratic socialism. Then came the new idea of the renaissance of religious socialism. Religious socialism has been born since the prophets were sent as caliphs on earth. Learning from the history of the struggle of the saints who sought to liberate the oppressed, religious socialism tries to pave the way for an alternative solution, the fourth path. Ali Shari'ati first used this paradigm when he strongly criticized the two great ideological empires, socialism and capitalism. According to Shari'ati, socialism in Islam can be functionalized into a central view of life philosophy of bringing people to make social changes. In terms of international politics, Ali Shari'ati's Religious Socialism was more closely associated with the political constructs that were taking place in Iran at the time. By using a qualitative approach and making his scattered works and thoughts and expert analysis related to his thoughts the sources in this article. Di awal abad ke-19, masyarakat dunia memasuki babak baru kehidupan. Namun era ini melahirkan wajah sosial yang lebih kompleks dan “mahal”. Industri merupakan bentuk utama yang menggenangi pekarangan masyarakat. Semangat individualisme menciptakan kuasi-biner oposisi. Kuat-lemah, kaya-miskin. Satu persatu berbagai ideologi muncul sebagai antitesis Kapitalisme, mulai dari sosialisme-komunisme hingga sosialisme demokratik. Kemudian muncul ide baru tentang renaisans sosialisme religius. Sosialisme keagamaan telah lahir sejak para nabi diutus sebagai khalifah di muka bumi. Belajar dari sejarah perjuangan para wali yang berusaha membebaskan kaum tertindas, sosialisme religius mencoba membuka jalan bagi solusi alternatif, jalan keempat. Ali Syari'ati pertama kali menggunakan paradigma ini ketika mengkritik keras dua imperium ideologi besar, sosialisme dan kapitalisme. Menurut Syari'ati, sosialisme dalam Islam dapat difungsikan menjadi pandangan sentral falsafah hidup yang membawa manusia melakukan perubahan sosial. Dalam politik internasional, Sosialisme Keagamaan Ali Syari'ati lebih erat kaitannya dengan konstruksi politik yang sedang berlangsung di Iran saat itu. Dengan menggunakan pendekatan kualitatif dan menjadikan karaya-karaya serta pemikirannya yang tersebar dan Analisa para ahli terkait pemikirannya menjadi sumber dalam artikel ini.
Information spread through social networks is ubiquitous. Influence maximiza- tion (IM) algorithms aim to identify individuals who will generate the greatest spread through the social network if provided with information, and have been largely devel- oped with marketing in mind. In social networks with community structure, which are very common, IM algorithms focused solely on maximizing spread may yield signifi- cant disparities in information coverage between communities, which is problematic in settings such as public health messaging. While some IM algorithms aim to remedy disparity in information coverage using node attributes, none use the empirical com- munity structure within the network itself, which may be beneficial since communities directly affect the spread of information. Further, the use of empirical network struc- ture allows us to leverage community detection techniques, making it possible to run fair-aware algorithms when there are no relevant node attributes available, or when node attributes do not accurately capture network community structure. In contrast to other fair IM algorithms, this work relies on fitting a model to the social network which is then used to determine a seed allocation strategy for optimal fair information spread. We develop an algorithm to determine optimal seed allocations for expected fair coverage, defined through maximum entropy, provide some theoretical guarantees under appropriate conditions, and demonstrate its empirical accuracy on both simu- lated and real networks. Because this algorithm relies on a fitted network model and not on the network directly, it is well-suited for partially observed and noisy social networks.
ABSTRACT In the historical struggle between Marxism and anarchism, Lenin played an important role. In “Anarchism and Socialism,” written in 1901, Lenin denounced anarchism as a petty bourgeois ideology. Lenin defended this view over the next twenty years, as he fought for Bolshevik hegemony in Russia. This article argues that Lenin’s struggle against anarchism was significant for several reasons. First, it clarified the fundamental differences between anarchism and Marxism. Second, Lenin contributed to the victory of Marxism over anarchism, initially, in revolutionary Russia, and after that, within the Soviet era communist movement. Third, Lenin’s struggle offers original insights. For one thing, Lenin delineated the revolutionary limitations of anarchism. He identified the circumstances in which anarchism can empower or weaken the working class. Lenin also established the organisation, struggle, and leading role of the vanguard working class party as an independent distinction between Marxism and anarchism, a distinction that needs resurrecting today.
Stephany Rajeh, Marinette Savonnet, Eric Leclercq
et al.
Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both type of centrality measures are computed. Results show that community-aware centrality measures can be divided into two groups. The first group, which includes Bridging centrality, Community Hub-Bridge and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary across networks.
Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North
Social movements are dominated by storytelling, as narratives play a key role in how communities involved in these movements shape their identities. Thus, recognizing the accepted narratives of different communities is central to understanding social movements. In this context, journalists face the challenge of making sense of these emerging narratives in social media when they seek to report social protests. Thus, they would benefit from support tools that allow them to identify and explore such narratives. In this work, we propose a narrative extraction algorithm from social media that incorporates the concept of community acceptance. Using our method, we study the 2021 Cuban protests and characterize five relevant communities. The extracted narratives differ in both structure and content across communities. Our work has implications in the study of social movements, intelligence analysis, computational journalism, and misinformation research.