The article considers systematized threats to social health of the youth occurring as a result of their dependence from using digital devices and immersion into Internet space. The social health is defined as determined by internal and external factors state of readiness, opportunities and needs of the youth to implement social activities through building communications in society. One of threats undermining social health of representatives of this demographic group is digital addiction, resulting from immersion of the addict in digital environment using various digital devices. Such addiction can cause significant harm to both physical and social psychological health, starting from very young age that is confirmed by results of original empirical study. The most destructive consequence of this type of addiction is degradation of social and cognitive skills in young people, leading to problems with education, socialization, psychological affects, up to complete rejection of existing social norms and values. Such individuals are at most vulnerable to political manipulations in conditions of existing civilizational contradictions. They are, in course of information war, the first ones to be involved in processes destructive for state security. In this regard, it seems reasonable henceforth to profoundly investigate relationship between digital addiction and process of destruction of traditional values in rising generation and to substantiate technology of social immunization a tool to form and develop in the modern youth spiritual moral support, knowledge, skills and abilities to consciously resist destructive impact of digital environment.
Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.
Gaspard Abel, Argyris Kalogeratos, Jean-Pierre Nadal
et al.
The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.
The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework that extends the classical SEIZ (Susceptible-Exposed-Infected-Skeptic) epidemic model to capture the dynamics of toxic message propagation. Our simulator incorporates active moderation mechanisms through two distinct variants: a basic moderator, which implements uniform, non-personalized interventions, and smart moderator, which leverages user-specific psychological profiles based on Dark Triad traits to apply personalized, threshold-driven moderation. By varying parameter configurations, the simulator allows for systematic exploration of how different moderation strategies influence user state transitions over time. Simulation results demonstrate that while generic interventions can curb toxicity under certain conditions, profile-aware moderation proves significantly more effective in limiting both the spread and persistence of toxic behavior. This simulation framework offers a flexible and extensible tool for studying and designing adaptive moderation strategies in complex online social systems.
Misinformation is a growing concern in a decade involving critical global events. While social media regulation is mainly dedicated towards the detection and prevention of fake news and political misinformation, there is limited research about religious misinformation which has only been addressed through qualitative approaches. In this work, we study the spread of fabricated quotes (Hadith) that are claimed to belong to Prophet Muhammad (the prophet of Islam) as a case study demonstrating one of the most common religious misinformation forms on Arabic social media. We attempt through quantitative methods to understand the characteristics of social media users who interact with fabricated Hadith. We spotted users who frequently circulate fabricated Hadith and others who frequently debunk it to understand the main differences between the two groups. We used Logistic Regression to automatically predict their behaviors and analyzed its weights to gain insights about the characteristics and interests of each group. We find that both fabricated Hadith circulators and debunkers have generally a lot of ties to religious accounts. However, circulators are identified by many accounts that follow the Shia branch of Islam, Sunni Islamic public figures from the gulf countries, and many Sunni non-professional pages posting Islamic content. On the other hand, debunkers are identified by following academic Islamic scholars from multiple countries and by having more intellectual non-religious interests like charity, politics, and activism.
Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content varies by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.
Curt Stutsman, Eliot W. Robson, Abhishek K. Umrawal
In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate. In this paper, we propose a novel diffusion model -- the Pressure Threshold model (PT) -- for dynamically simulating the spread of influence through a social network. This model extends the popular Linear Threshold (LT) model by adjusting a node's outgoing influence in proportion to the influence it receives from its activated neighbors. We examine the Influence Maximization (IM) problem under this framework, which involves selecting seed nodes that yield maximal graph coverage after a diffusion process, and describe how the problem manifests under the PT model. Experiments on real-world networks, supported by enhancements to the open-source network-diffusion library CyNetDiff, reveal that greedy IM under PT can yield seed sets distinct from those under LT. Furthermore, the analyses show that densely connected networks amplify pressure effects far more strongly than sparse networks.
This service is intended to assist and develop the capacity of the Pekanbaru City Election Supervisory Board in supervising the implementation of the general election so that it runs honestly and fairly. One of the obstacles was identified in monitoring is the lack of public participation in reporting election violations. This is thought to be due to the unavailability of easily accessible reporting media. Regarding these problems, this service team was created and submitted a prototype of an application for reporting election violations to Pekanbaru Bawaslu. This application can be accessed easily by the public and through this application the report as soon as it is sent can be immediately known by the Pekanbaru Bawaslu admin. This service is carried out by: 1) Making a prototype of a violation report application to Pekanbaru Bawaslu; 2) Disseminate the use of the Application to Pekanbaru Bawaslu and others; 3) Simulating the use of the Election Violation reporting application.
Social history and conditions. Social problems. Social reform, Communities. Classes. Races
Endra Wijaya, Cipta Indralestari Rachman, Diani Kesuma
et al.
Kesadaran hukum masyarakat desa dapat dibina melalui pelatihan paralegal yang melibatkan warga desa dan aparat pemerintah desa sebagai pesertanya. Kesadaran hukum merupakan unsur yang penting dalam proses penegakan hukum. Artikel ini memaparkan mengenai kegiatan pengabdian kepada masyarakat dalam bentuk pelatihan paralegal yang diselenggarakan oleh Fakultas Hukum Universitas Pancasila bekerja sama dengan beberapa lembaga negara serta pemerintah, dan juga lembaga swadaya masyarakat. Para peserta pelatihan paralegal ini ialah warga masyarakat dan perangkat pemerintah Desa Karang Suraga dan Desa Bulakan, Provinsi Banten. Simpulan yang dapat diambil ialah bahwa pelatihan paralegal merupakan program yang penting bagi upaya menumbuhkembangkan pemahaman, kesadaran serta keterampilan masyarakat di bidang hukum. Melalui pelatihan paralegal, masyarakat dan aparat desa dibekali pemahaman mengenai hukum yang kelak dapat mereka manfaatkan untuk merespons dan menyelesaikan problem hukum yang mereka hadapi sehari-hari.
Social history and conditions. Social problems. Social reform, Communities. Classes. Races
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Sina Abdidizaji
et al.
The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread.
MY Teguh Sulistyono, Wellia Shinta Sari, Siti Hadiati Nugraini
et al.
Perguruan Tinggi adalah salah satu lembaga pendidikan yang membatu dan berperan aktif di dalam mencerdaskan kehidupan bangsa dengan mentransfer ilmu pengetahuan atau knowledge transfer kepada lembaga-lembaga pendidikan lain, lembaga-lembaga lain baik yang setara ataupun yang berada di bawahnya, baik pendidikan formal atau non formal. Salah satu industri yang membutuhkan knowledge transfer adalah Industri Kecil Menengah (IKM). IKM yang dijadikan mitra adalah IKM milik Ibu Siti Rokhanah yang berlokasi di Kelurahan Nawangsari Kecamatan Weleri Kabupaten Kendal. Berjarak kurang lebih 60 km dari Universitas Dian Nuswantoro Semarang. IKM katering dan snak (kue tradisional) yang mengalami masalah yaitu masalah pemasaran produk, produksi katering dan snack (kue tradisional), masalah manajemen usaha dan masalah manajemen keuangan. Masalah pemasaran produk terjadi karena dilakukan dari mulut ke mulut sehingga target penjualan tidak tercapai, masalah produksi terjadi karena masih menggunakan alat memasak tradisional dan tidak adanya oven yang digunakan dalam produksi sehingga pemanasan yang tidak merata dan proses produksinya berulang-ulang, masalah manajemen usaha tidak adanya pengelolaan sumberdaya yang baik untuk jalannya sebuah industri dan masalah manajemen keuangan tidak adanya pencatatan keluar masuk keuangan sehingga tidak diketahui rugi dan labanya. Untuk membantu mengatasi keempat masalah tersebut maka diusulkan dengan memperbaiki proses pemasaran, proses produksi, proses manajemen usaha dan proses manajemen keuangan. Untuk memperbaiki keempat proses tersebut yaitu untuk pemasaran diadakan pemasaran melalui media internet, brosur, MMT dan media sosial, untuk manajemen usaha dan manajemen keuangan dilakukan pelatihan manajemen keuangan, manajemen usaha dan pelatihan pemasangan iklan di internet dan media sosial, sedangkan untuk proses produksi pembuatan alat masak dengan penggunaan oven otomatis dengan panas merata. Hasil akhir dari Program Kemitraan Masyarakat ini adalah pemanfaatan sumberdaya Industri Kecil Menengah (IKM) dalam meningkatkan produktivitas dan pemasaran produk melalui transformasi digital masyarakat desa.
Social history and conditions. Social problems. Social reform, Communities. Classes. Races
Artykuł koncentruje się na analizie pedagogii Jespera Juula, duńskiego pedagoga i terapeuty. Podstawowym wątkiem jest rekonstrukcja jego koncepcji życia rodzinnego w odniesieniu do dwóch kluczowych składników: kompetencji oraz przywództwa. Celem jest analiza tych wątków jego twórczości, które odnoszą się do problematyki kryzysu rodziny i dróg jego przezwyciężenia, które proponuje Juul. Podstawową metodą stosowaną w tekście jest krytyczna analiza źródeł. Pozwala to dostrzec, że rodzicielskie przywództwo nie musi stać w opozycji do rozwoju kompetencji dziecka, lecz może być istotnym jego katalizatorem.
Social history and conditions. Social problems. Social reform, Education
Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.
This article traces the development of the European social policy from its minimalist role provided for in the Treaties of Rome, through the neoliberal orientation, especially in the financial crisis of 2008, to the trend towards a socio-investment perspective in response to the crisis caused by the Kovid19 pandemic. Current EU social policy applies a new approach that emphasizes the need to invest in education and training and the provision of quality social services throughout the life course of people - from early childhood to old age. It relies on the mutual enrichment of national and supranational forms of social protection.
Social history and conditions. Social problems. Social reform
The article considers problems of professional burnout of medical workers. On the basis of survey of students of secondary and higher educational institutions, their positions regarding risks of professional burnout in future professional activities were determined. The study demonstrated that it is necessary to include into educational process of training physicians and specialists of secondary and junior medical personnel discipline focused on development of appropriate professional competencies to prevent risk of professional burnout and to reduce stress and psychological overload.
We draw insights from the social psychology literature to identify two facets of Twitter deliberations about migrants, i.e., perceptions about migrants and behaviors towards mi-grants. Our theoretical anchoring helped us in identifying two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. We have employed unsuper-vised and supervised approaches to identify these perceptions and behaviors. In the domain of applied NLP, our study of-fers a nuanced understanding of migrant-related Twitter de-liberations. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models. Additionally, we argue that tweets con-veying antipathy or animosity can be broadly considered hate speech towards migrants, but they are not the same. Thus, our approach has fine-tuned the binary hate speech detection task by highlighting the granular differences between perceptual and behavioral aspects of hate speeches.
Chrysoula Terizi, Despoina Chatzakou, Evaggelia Pitoura
et al.
Cyberaggression has been studied in various contexts and online social platforms, and modeled on different data using state-of-the-art machine and deep learning algorithms to enable automatic detection and blocking of this behavior. Users can be influenced to act aggressively or even bully others because of elevated toxicity and aggression in their own (online) social circle. In effect, this behavior can propagate from one user and neighborhood to another, and therefore, spread in the network. Interestingly, to our knowledge, no work has modeled the network dynamics of aggressive behavior. In this paper, we take a first step towards this direction by studying propagation of aggression on social media using opinion dynamics. We propose ways to model how aggression may propagate from one user to another, depending on how each user is connected to other aggressive or regular users. Through extensive simulations on Twitter data, we study how aggressive behavior could propagate in the network. We validate our models with crawled and annotated ground truth data, reaching up to 80% AUC, and discuss the results and implications of our work.