Hasil untuk "Social pathology. Social and public welfare. Criminology"

Siba Prasada Panigrahi, Deepika Swain

This article examines the behaviour of unethical leaders in public organizations on two important outcomes: first, impact of behaviour that drags Government into courts and second, impact of behaviour responsible for public money. The authors, based on theoretical background, introduce the nomenclature of “Anti Heroes” and develop the hypothesis that these heroes have three faces one each for the destructive leadership, the corporate psychopathy and the white-collar criminality. In this study, the participants (N = 436) challenged the leadership from of a sample of 4000 disposed cases in Indian courts. Findings reveal a nexus between all the dark personalities and the resultant behaviour emerge as root cause in litigating the Government and wasting public money.

Benjamin White, Anastasia Shimorina

Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at COLM 2025.

Leonardo Koury Martins

Este artigo tem o objetivo de dialogar sobre a comunicação enquanto um direito humano a partir dos posicionamentos construídos pelas entidades representativas do Serviço Social e sobre como os assistentes sociais podem se somar na defesa do referido direito com base na estratégia da democratização da comunicação no Brasil no cotidiano do trabalho profissional. O cenário do direito à comunicação e a sua democratização têm sido pautados pelos movimentos sociais desde os anos de 1980, quando, embora a comunicação tenha começado a constar como um direito constitucional, a sua regulamentação não ocorreu por completo, o que garantiu a apropriação privada do citado direito por parte das elites nacionais. Na atualidade, as redes privadas nacionais de comunicação, em conjunto com as big techs, integram a internacionalização do domínio da informação em grande escala, que se aproveita do cenário de notícias falsas e do deserto provocado pela desinformação, o que afeta a vida da população brasileira. 

Noura Erakat

Between 2016 and 2018, Black, Palestinian and Jewish organizations, under the banner of the Demilitarize! Durham 2 Palestine coalition, led a campaign in Durham, North Carolina, that successfully passed a City Council resolution prohibiting US police exchanges with Israel. Based on direct interviews with the activists who led the campaign, this article sets out to trace the history of the Demilitarize! Effort, detailing its chronological developments with an eye on highlighting how Black–Palestinian solidarity continues to function as an anti-imperial analytic. Particularly, it illuminates how settler colonialism unsettles the demarcation between foreign and domestic frontiers thus entwining military and police force expressed in transnational state violence against racialized communities. In doing so, the article will offer and preserve a movement archive developed by activists in Durham. The Demilitarize! Durham 2 Palestine coalition is built upon a rich legacy of local Palestine solidarity activism and its coalitionary efforts focused on a narrative of racialized state violence that directly connected militarized US law enforcement to trainings in Israel thus illuminating the local manifestations of US empire. This article also seeks to use the movement archive to consider how seemingly formidable circuits of state violence that undergird imperial domination are simultaneously vulnerable to attack and dismantlement.

N. V. Mikhaleva

According to the Russian legislation an expert of a state forensic institution must get an additional professional education in a specific expert specialty. One of the important disciplines studied by a future expert is the Theory of Forensic Expertise, which now has transformed into Forensic Expertology. The article analyses a textbook “Fundamentals of Forensic Expertology” prepared in the Russian Federal Centre of Forensic Science under the Ministry of Justice of Russia for students who receive additional professional training in expert specialties, which is basic for the corresponding course of lectures. The author outlines some practical steps necessary for the transition to training the future experts in the basics of forensic expertology.

Jianpeng Yao, Xiaopan Zhang, Yu Xia et al.

Reinforcement learning (RL) enables social robots to generate trajectories without relying on human-designed rules or interventions, making it generally more effective than rule-based systems in adapting to complex, dynamic real-world scenarios. However, social navigation is a safety-critical task that requires robots to avoid collisions with pedestrians, whereas existing RL-based solutions often fall short of ensuring safety in complex environments. In this paper, we propose SoNIC, which to the best of our knowledge is the first algorithm that integrates adaptive conformal inference (ACI) with constrained reinforcement learning (CRL) to enable safe policy learning for social navigation. Specifically, our method not only augments RL observations with ACI-generated nonconformity scores, which inform the agent of the quantified uncertainty but also employs these uncertainty estimates to effectively guide the behaviors of RL agents by using constrained reinforcement learning. This integration regulates the behaviors of RL agents and enables them to handle safety-critical situations. On the standard CrowdNav benchmark, our method achieves a success rate of 96.93%, which is 11.67% higher than the previous state-of-the-art RL method and results in 4.5 times fewer collisions and 2.8 times fewer intrusions to ground-truth human future trajectories as well as enhanced robustness in out-of-distribution scenarios. To further validate our approach, we deploy our algorithm on a real robot by developing a ROS2-based navigation system. Our experiments demonstrate that the system can generate robust and socially polite decision-making when interacting with both sparse and dense crowds. The video demos can be found on our project website: https://sonic-social-nav.github.io/.

Bassi Andrea

A. Ya. Asnis, Sh. N. Khaziev

The problem of establishing the identity of the anonymous author has always been a pressing task of criminalistics, forensic examination, literary and art studies. Since the end of the 19th century forensic scientists have been developing technical and tactical methods and means to recognize a person who is the author or at the same time author and performer of handwritten and typed texts, drawings, and audio messages. At present tactical, technical, and forensic support for the identification of the unknown author of computer materials, telecommunication messages and mailings are actively developing. Also, methods for applying artificial intelligence technologies during the investigative activities of this particular kind are being developed. The present article is devoted to an overview of the history, current state and prospects for the development of methods for obtaining information about a person from anonymous text and speech materials.

IBCCRIM

A ideia de inserção de fórmulas negociais no processo penal sempre gerou controvérsia. A cada nova proposta de lei buscando a abreviação do processo ou a obtenção de provas por meios consensuais, renovam-se as críticas relativas à suposta sobreposição de critérios de efetividade em relação aos critérios de justiça.

Fabian Teichmann, Sonia Boticiu, Bruno S. Sergi

This paper examines the intersection of international arbitration and money laundering and corruption in jurisdictions where corruption is endemic, such as some Eastern European countries, to provide insight into the challenges and risks involved in arbitrating. Few sources have addressed the issue of money laundering and corruption in international arbitration in these jurisdictions. Given this significant research gap, the aim of this paper is to draw attention to areas where investment and international trade relations may be undermined by criminal activity. To this end, the specific methods used by money launderers were explored through a qualitative study involving ten suspected money launderers and eighteen prevention experts. The findings were then tested quantitatively. The results show that, as the number of corruption allegations in arbitration has increased, arbitrators have assumed an important role in reviewing compliance rules related to corruption. As a result, this article seeks to improve current understanding of compliance issues in arbitration in countries where corruption is prevalent and thereby identify a path for future research.

Federico Vazquez

This opening editorial aims to interest researchers and encourage novel research in the closely related fields of sociophysics and computational social science. We briefly discuss challenges and possible research directions in the study of social phenomena, with a particular focus on opinion dynamics. The aim of this special issue is to allow physicists, mathematicians, engineers and social scientists to show their current research interests in social dynamics, as well as to collect recent advances and new techniques in the analysis of social systems.

Hana Matatov, Mor Naaman, Ofra Amir

The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena related to the spread of such content. In this paper, we address this need by building a dataset of social media images and evaluating visual near-duplicates retrieval methods based on image retrieval and several advanced visual feature extraction methods. We evaluate the methods using a large-scale dataset of images we crawl from social media and their manipulated versions we generated, presenting promising results in terms of recall. We demonstrate the potential of this method in two case studies: one that shows the value of creating systems supporting manual content review, and another that demonstrates the usefulness of automatic large-scale data analysis.

Chiyu Zhang, Muhammad Abdul-Mageed, El Moatez Billah Nagoudi

With the proliferation of social media, many studies resort to social media to construct datasets for developing social meaning understanding systems. For the popular case of Twitter, most researchers distribute tweet IDs without the actual text contents due to the data distribution policy of the platform. One issue is that the posts become increasingly inaccessible over time, which leads to unfair comparisons and a temporal bias in social media research. To alleviate this challenge of data decay, we leverage a paraphrase model to propose a new persistent English Twitter dataset for social meaning (PTSM). PTSM consists of $17$ social meaning datasets in $10$ categories of tasks. We experiment with two SOTA pre-trained language models and show that our PTSM can substitute the actual tweets with paraphrases with marginal performance loss.

Alexandria Volkening

Traffic jams on roadways, echo chambers on social media, crowds of moving pedestrians, and opinion dynamics during elections are all complex social systems. These applications may seem disparate, but some of the questions that they motivate are similar from a mathematical perspective. Across these examples, researchers seek to uncover how individual agents -- whether drivers, Twitter accounts, pedestrians, or voters -- are interacting. By better understanding these interactions, mathematical modelers can make predictions about the group-level features that will emerge when agents alter their behavior. In this tutorial, which is based on the lecture that I gave at the 2021 American Mathematical Society Short Course, I introduce some of the terms, methods, and choices that arise when building such data-driven models. I discuss the differences between models that are statistical or mathematical, static or dynamic, spatial or non-spatial, discrete or continuous, and phenomenological or mechanistic. For concreteness, I also describe models of two complex systems, election dynamics and pedestrian-crowd movement, in more detail. With a conceptual approach, I broadly highlight some of the challenges that arise when building and calibrating models, choosing complexity, and working with quantitative and qualitative data.

Fujun Hou

I present an example in which the individuals' preferences are strict orderings, and under the majority rule, a transitive social ordering can be obtained and thus a non-empty choice set can also be obtained. However, the individuals' preferences in that example do not satisfy any conditions (restrictions) of which at least one is required by Inada (1969) for social preference transitivity under the majority rule. Moreover, the considered individuals' preferences satisfy none of the conditions of value restriction (VR), extremal restriction (ER) or limited agreement (LA), some of which is required by Sen and Pattanaik (1969) for the existence of a non-empty social choice set. Therefore, the example is an exception to a number of theorems of social preference transitivity and social choice set existence under the majority rule. This observation indicates that the collection of the conditions listed by Inada (1969) is not as complete as might be supposed. This is also the case for the collection of conditions VR, ER and LA considered by Sen and Pattanaik (1969). This observation is a challenge to some necessary conditions in the current social choice theory. In addition to seeking new conditions, one possible way to deal with this challenge may be, from a theoretical prospective, to represent the identified conditions (such as the VR, ER and LA) in terms of a common mathematical tool, and then, people may find more.

Firoj Alam, Tanvirul Alam, Muhammad Imran et al.

Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for a faster disaster response. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. To develop robust real-time models, it is necessary to understand the capability of the publicly available pre-trained models for these tasks, which remains to be under-explored in the crisis informatics literature. In this study, we address such limitations by investigating ten different network architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore various data augmentation strategies, semi-supervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results.

Régis Ebeling, Carlos Abel Córdova Sáenz, Jeferson Nobre et al.

The outbreak of COVID-19 had a huge global impact, and non-scientific beliefs and political polarization have significantly influenced the population's behavior. In this context, COVID vaccines were made available in an unprecedented time, but a high level of hesitance has been observed that can undermine community immunization. Traditionally, anti-vaccination attitudes are more related to conspiratorial thinking rather than political bias. In Brazil, a country with an exemplar tradition in large-scale vaccination programs, all COVID-related topics have also been discussed under a strong political bias. In this paper, we use a multidimensional analysis framework to understand if anti/pro-vaccination stances expressed by Brazilians in social media are influenced by political polarization. The analysis framework incorporates techniques to automatically infer from users their political orientation, topic modeling to discover their concerns, network analysis to characterize their social behavior, and the characterization of information sources and external influence. Our main findings confirm that anti/pro stances are biased by political polarization, right and left, respectively. While a significant proportion of pro-vaxxers display haste for an immunization program and criticize the government's actions, the anti-vaxxers distrust a vaccine developed in a record time. Anti-vaccination stance is also related to prejudice against China (anti-sinovaxxers), revealing conspiratorial theories related to communism. All groups display an "echo chamber behavior, revealing they are not open to distinct views.

Sylvie Castanié, Maria Teresa Munoz Sastre, Lonzozou Kpanake et al.

Abstract Background Public authorities resort to various control policies in order to curb the prevalence of unhealthy behaviors. As these policies can only succeed to the extent that people agree with them, this study mapped French people’s positions regarding restrictive control policies in general. Method A sample of 344 adults (among them health professionals and lawyers) were presented with 54 vignettes depicting a control policy. Each vignette contained four pieces of information: the type of addictive behavior targeted (smoking, drinking, or gambling), the nature of preventive measures (e.g., information campaigns), the degree of regulative measures (e.g., prohibition to minors), and the severity of sanctions. Results Through cluster analysis, eight qualitatively different positions were found: Never acceptable (9%), Weak or moderate regulation (5%), Moderate regulation associated with strong prevention (11%), Strong or moderate regulation (11%), Strong regulation in association with strong prevention (23%), Moderate sanctions in association with strong prevention and moderate regulation (9%), Severe sanctions (9%), and Always acceptable (9%). Some participants (14%) expressed no opinion at all. Conclusion French people’s positions regarding control policies were extremely diverse. Regarding tobacco, however, one type of policy would likely be supported by a majority of people: Moderate regulation associated with at least a moderate level of prevention and low-level sanctions. Regarding alcohol, an acceptable position would be: Moderate regulation associated with at least a moderate level of prevention and high-level sanctions. Regarding gambling, an acceptable position would be: Strong regulation associated with at least a moderate level of prevention and low-level sanctions.

Mehmet Simsek, Henning Meyerhenke

Influence Maximization (IM) aims at finding the most influential users in a social network, i. e., users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i. e., the ability to identify influential users) of centrality measures only in restricted settings, e. g., in undirected/unweighted networks and/or within a propagation model less common for IM. In this paper, we first show that good results within the Susceptible- Infected-Removed (SIR) propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly. social networks, influence maximization, centrality measures, IC propagation model, influential spreaders

Douglas Guilbeault, Samuel Woolley, Joshua Becker

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluate the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and entrenched polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.