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

Hossein Salemi, Jitin Krishnan, Hemant Purohit

Attribution theory explains how individuals interpret and attribute others' behavior in a social context by employing personal (dispositional) and impersonal (situational) causality. Large Language Models (LLMs), trained on human-generated corpora, may implicitly mimic this social attribution process in social contexts. However, the extent to which LLMs utilize these causal attributions in their reasoning remains underexplored. Although using reasoning paradigms, such as Chain-of-Thought (CoT), has shown promising results in various tasks, ignoring social attribution in reasoning could lead to biased responses by LLMs in social contexts. In this study, we investigate the impact of incorporating a user's goal as knowledge to infer dispositional causality and message context to infer situational causality on LLM performance. To this end, we introduce a scalable method to mitigate such biases by enriching the instruction prompts for LLMs with two prompt aids using social-attribution knowledge, based on the context and goal of a social media message. This method improves the model performance while reducing the social-attribution bias of the LLM in the reasoning on zero-shot classification tasks for behavior analytics applications. We empirically show the benefits of our method across two tasks-intent detection and theme detection on social media in the disaster domain-when considering the variability of disaster types and multiple languages of social media. Our experiments highlight the biases of three open-source LLMs: Llama3, Mistral, and Gemma, toward social attribution, and show the effectiveness of our mitigation strategies.

Chuka Emezue

Abstract Background Firearm violence remains a critical public health crisis that disproportionately affects Black children and youth, National data show that Black children experience firearm homicide rates roughly eight times higher than White children, and Black males ages 18–24 face rates nearly 23 times higher than their White peers. Although aggression is a well-established correlate of attitudes supportive of gun violence, far less is known about how cognitive–emotional processes and states (e.g., psychological distress) shape these associations among youth frequently exposed to community violence but not involved in the justice system. This study addresses this gap by examining whether psychological distress moderates the relationship between aggression profiles and attitudes toward gun violence. Objectives This exploratory study tested whether psychological distress moderates the associations between proactive aggression (PA), reactive aggression (RA), and attitudes toward guns and violence (AGVQ™). We also assessed the independent predictive effects of PA, RA, psychological distress, and depressive symptoms (PHQ-8) on AGVQ scores. Methods Baseline data were drawn from a pilot intervention study (N = 70) involving young adults at elevated risk for violence and substance use. PA and RA were modeled as independent variables, with psychological distress as a moderator. Correlation analyses and multiple linear regression were conducted in JASP (v0.18.3), and moderation analyses were performed in Python (Google Colab). Results AGVQ scores were strongly correlated with PA (r = .80, p < .001), RA (r = .70, p < .001), psychological distress (r = .69, p < .001), depressive symptoms (r = .69, p < .001), and past-year violence exposure (r = .67, p < .001). In regression analyses, PA significantly predicted AGVQ scores (b = 1.19, p < .001), whereas RA (p = .81) and psychological distress (p = .176) were not significant predictors. Moderation analyses indicated that psychological distress marginally attenuated the association between RA and AGVQ (b = − 0.05, p = .084), while no significant interaction emerged for PA. Conclusions These preliminary findings underscore the importance of distinguishing aggression subtypes when examining firearm-related attitudes among youth facing chronic adversity. Proactive aggression emerged as the most robust predictor of gun-supportive attitudes, independent of psychological distress, highlighting the need for interventions that target cognitive–emotional mechanisms underlying instrumental aggression. Future research should incorporate larger samples and examine additional sociodemographic and distress-related moderators to refine theory-driven models of firearm violence risk in marginalized populations.

Augustinas Jučas, Chirag Raman

Adaptively forecasting human behavior in social settings is an important step toward achieving Artificial General Intelligence. Most existing research in social forecasting has focused either on unfocused interactions, such as pedestrian trajectory prediction, or on monadic and dyadic behavior forecasting. In contrast, social psychology emphasizes the importance of group interactions for understanding complex social dynamics. This creates a gap that we address in this paper: forecasting social interactions at the group (conversation) level. Additionally, it is important for a forecasting model to be able to adapt to groups unseen at train time, as even the same individual behaves differently across different groups. This highlights the need for a forecasting model to explicitly account for each group's unique dynamics. To achieve this, we adopt a meta-learning approach to human behavior forecasting, treating every group as a separate meta-learning task. As a result, our method conditions its predictions on the specific behaviors within the group, leading to generalization to unseen groups. Specifically, we introduce Social Process (SP) models, which predict a distribution over future multimodal cues jointly for all group members based on their preceding low-level multimodal cues, while incorporating other past sequences of the same group's interactions. In this work we also analyze the generalization capabilities of SP models in both their outputs and latent spaces through the use of realistic synthetic datasets.

Yutong Zhang, Taeuk Kang, Sydney Yeh et al.

Positive social interactions can occur in groups of many shapes and sizes, spanning from small and private to large and open. However, social media tends to binarize our experiences into either isolated small groups or into large public squares. In this paper, we introduce Burst, a social media design that allows users to share and curate content between many spaces of varied size and composition. Users initially post content to small trusted groups, who can then burst that content, routing it to the groups that would be the best audience. We instantiate this approach into a mobile phone application, and demonstrate through a ten-day field study (N=36) that Burst enabled a participatory curation culture. With this work, we aim to articulate potential new design directions for social media sharing.

Nur Shazwani Kamarudin, Ghazaleh Beigi, Lydia Manikonda et al.

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.

Marcos Afonso Johner

Desde a Constituição de 1988, separadas estão as funções de acusar e de julgar o caso penal (art. 129, I). Com o advento da Lei n. 13.964/2019, o legislador expressamente atribuiu ao direito processual penal a estrutura acusatória, com o propósito de vedar a substituição do órgão de acusação pelo magistrado, retirando, deste último, poderes ex officio, como, v.g., para deflagrar a ação penal, decretar medidas cautelares e produzir provas. Nesse contexto, debate-se acerca da (in)compatibilidade do art. 385 do CPP, que permite ao juiz condenar mesmo nos casos em que o Ministério Público opina pela absolvição, com a Constituição e a estrutura acusatória. O artigo explora a problemática, indicando os argumentos favoráveis à validade do dispositivo em questão, bem como os argumentos que o reputam incompatível com o processo penal acusatório. Por fim, o artigo propõe uma solução interpretativa para o art. 385 do CPP.

Victor Siqueira Serra

O texto aborda os desafios da escolarização de adolescentes e jovens atendidos pelo Serviços de Medidas Socioeducativas em Meio Aberto do Rio Pequeno. Por meio de um estudo de caso institucional, analisou-se as barreiras enfrentadas por esses adolescentes e jovens em seu território, como estigmatização, vulnerabilidade socioeconômica, defasagem escolar e a desconexão entre práticas pedagógicas e realidades vividas. A conclusão reforça a necessidade de políticas públicas integradas e formação continuada para educadores, promovendo uma educação dialógica e inclusiva que vá além do cumprimento normativo, garantindo suporte individualizado e um ambiente escolar acolhedor e transformador.

Josinete de Carvalho Bezerra, Ana Cristina Brito Arcoverde

Resumo: Este artigo apresenta a Rede de Filantropia para a Justiça Social, enfatizando seu funcionamento, função e ideologias nela reproduzidas para garantir a hegemonia das classes dominantes. O debate desenvolve uma análise bibliográfica sobre aparelhos privados de hegemonia e demais conceitos e categorias relacionadas ao termo, tendo por base, principalmente, estudos gramscianos. A base analítica referenciada permite identificar especificidades do aparelho privado de hegemonia (APH) em destaque, em sua mediação com a visão de mundo construída.

Effrosyni Papanastasiou, Anastasios Giovanidis

Current network inference algorithms fail to generate graphs with edges that can explain whole sequences of node interactions in a given dataset or trace. To quantify how well an inferred graph can explain a trace, we introduce feasibility, a novel quality criterion, and suggest that it is linked to the result's accuracy. In addition, we propose CEM-*, a network inference method that guarantees 100% feasibility given online social media traces, which is a non-trivial extension of the Expectation-Maximization algorithm developed by Newman (2018). We propose a set of linear optimization updates that incorporate a set of auxiliary variables and a set of feasibility constraints; the latter takes into consideration all the hidden paths that are possible between users based on their timestamps of interaction and guide the inference toward feasibility. We provide two CEM-* variations, that assume either an Erdos Renyi (ER) or a Stochastic Block Model (SBM) prior for the underlying graph's unknown distribution. Extensive experiments on one synthetic and one real-world Twitter dataset show that for both priors CEM-* can generate a posterior distribution of graphs that explains the whole trace while being closer to the ground truth. As an additional benefit, the use of the SBM prior infers and clusters users simultaneously during optimization. CEM-* outperforms baseline and state-of-the-art methods in terms of feasibility, run-time, and precision of the inferred graph and communities. Finally, we propose a heuristic to adapt the inference to lower feasibility requirements and show how it can affect the precision of the result.

Lize Alberts, Ulrik Lyngs, Max Van Kleek

Technologies increasingly mimic human-like social behaviours. Beyond prototypical conversational agents like chatbots, this also applies to basic automated systems like app notifications or self-checkout machines that address or 'talk to' users in everyday situations. Whilst early evidence suggests social cues may enhance user experience, we lack a good understanding of when, and why, their use may be inappropriate. Building on a survey of English-speaking smartphone users (n=80), we conducted experience sampling, interview, and workshop studies (n=11) to elicit people's attitudes and preferences regarding how automated systems talk to them. We thematically analysed examples of phrasings/conduct participants disliked, the reasons they gave, and what they would prefer instead. One category of inappropriate behaviour we identified regards the use of social cues as tools for manipulation. We describe four unwanted tactics interfaces use: agents playing on users' emotions (e.g., guilt-tripping or coaxing them), being pushy, `mothering' users, or being passive-aggressive. Another category regards pragmatics: personal or situational factors that can make a seemingly friendly or helpful utterance come across as rude, tactless, or invasive. These include failing to account for relevant contextual particulars (e.g., embarrassing users in public); expressing obviously false personalised care; or treating a user in ways that they find inappropriate for the system's role or the nature of their relationship. We discuss these behaviours in terms of an emerging 'social' class of dark and anti-patterns. Drawing from participant recommendations, we offer suggestions for improving how interfaces treat people in interactions, including broader normative reflections on treating users respectfully.

Mohamed Ibrahim, Habiba Ibrahim

The world is experiencing unprecedented levels of humanitarian crises due to the effects of climate shocks, intra-state and inter-state armed conflict. As of 2020, there were more than 70 million displaced persons globally with the global pandemic making the situation worse. It is expected that the current economic downturn will roll back the gains made in gender equity and poverty level improvements. With old conflicts remaining unresolved, and new ones springing up across the globe, a critical question arises of the role of social workers as agents of conflict resolution and peacemakers in addition to their traditional role of providing psychosocial support. This paper attempts to provide perspectives on the need for mental health social workers to actively and explicitly engage in conflict resolution and peacebuilding. In doing so, the authors will discuss the case of the Wajir Women Peace and Development, a women led organization that shaped the role of local professionals in engaging and sustaining peace in Northern Kenya and beyond. The choice of this organization is based on the personal and work experience of the authors who hails from Wajir and worked as a health and teaching professionals in late 1990s to mid-2000s.

Alexandros Efstratiou, Emiliano De Cristofaro

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.

Jianan Chen, Qin Hu, Honglu Jiang

As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this social dilemma, we employ the Multi-player Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Experimental results validate that the MMZD strategy is effective in maximizing the social welfare.

Nurul Eka Hidayati, Fentiny Nugroho, Sali Rahadi Asih

The number of reported incidents of sexual violence continues to increase in Indonesia, giving rise to deep concerns and calls to prevent sexual violence from occurring in the future. Some experts call it “the Silent Epidemic” because it is estimated that the actual number of events is much higher than reported. Therefore, research on this issue needs to be continued to get input on the cause and effect, impact and prevention efforts. Researches on child sexual abuse and the impact throughout the lives of the survivors is highly important in providing scientific evidences for developing the interventions in social work practice. However, it also comes with very specific challenges when conducting a research on this issue. There are no specific guidelines regarding ethics and methodology in carrying out research on survivors of sexual violence.  Therefore, in the future it is important to consider the methodological and ethical aspects before embarking on a research involving survivors of sexual violence. Based on the experience of carrying out this research, the aspects that potentially harm or put participants and researchers at risk can be minimized or even avoided. This experience can also provide reinforcement that research involving trauma survivors can benefit both the affected population as well as social work scientists and practitioners. This manuscript discusses the current thinking on these issues.

Christiane dos Santos Luciano, Pamela Barreto Correa

Este artigo visa compreender o contexto atual da fome no Brasil. O país, apesar de ser um dos principais produtores e exportadores de alimentos do mundo, não garante condições de alimentação adequada para mais da metade de sua população. Apesar da responsabilidade do governo Bolsonaro pelos crescentes índices de insegurança alimentar, especialmente no período pandêmico, há que se buscar as raízes deste problema a partir de um olhar mais amplo, que leve em conta os elementos estruturais da particularidade da formação sócio-histórica brasileira. A contradição entre a grande produção de commodities para exportação e o aumento exponencial da fome se explica na relação dialética entre os determinantes externos e internos do capitalismo de tipo dependente. A produção de alimentos no Brasil não é voltada para as reais necessidades do povo brasileiro, mas orientada de acordo com o ritmo de exportação. Assim, a soberania alimentar e a reforma agrária popular são pautas que esbarram nos interesses do imperialismo e da burguesia nativa e só podem se efetivar nos marcos de uma ruptura com o próprio modo de produção capitalista, uma vez que na divisão internacional do trabalho, não resta aos países latinoamericanos outra alternativa de capitalismo que não seja o dependente.

Juan Pablo de Vicente, Alvaro Soto

Current datasets to train social behaviors are usually borrowed from surveillance applications that capture visual data from a bird's-eye perspective. This leaves aside precious relationships and visual cues that could be captured through a first-person view of a scene. In this work, we propose a strategy to exploit the power of current game engines, such as Unity, to transform pre-existing bird's-eye view datasets into a first-person view, in particular, a depth view. Using this strategy, we are able to generate large volumes of synthetic data that can be used to pre-train a social navigation model. To test our ideas, we present DeepSocNav, a deep learning based model that takes advantage of the proposed approach to generate synthetic data. Furthermore, DeepSocNav includes a self-supervised strategy that is included as an auxiliary task. This consists of predicting the next depth frame that the agent will face. Our experiments show the benefits of the proposed model that is able to outperform relevant baselines in terms of social navigation scores.

Tanvir Alam, Jens Schneider

The ahadith, prophetic traditions for the Muslims around the world, are narrations originating from the sayings and the deeds of Prophet Muhammad (pbuh). They are considered one of the fundamental sources of Islamic legislation along with the Quran. The list of persons involved in the narration of each hadith is carefully scrutinized by scholars studying the hadith, with respect to their reputation and authenticity of the hadith. This is due to the its legislative importance in Islamic principles. There were many narrators who contributed to this responsibility of preserving prophetic narrations over the centuries. But to date, no systematic and comprehensive study, based on the social network, has been adapted to understand the contribution of early hadith narrators and the propagation of hadith across generations. In this study, we represented the chain of narrators of the hadith collection from Sahih Bukhari as a social graph. Based on social network analysis (SNA) on this graph, we found that the network of narrators is a scale-free network. We identified a list of influential narrators from the companions as well as the narrators from the second and third-generation who contribute significantly in the propagation of hadith collected in Sahih Bukhari. We discovered sixteen communities from the narrators of Sahih Bukhari. In each of these communities, there are other narrators who contributed significantly to the propagation of prophetic narrations. We also found that most narrators were centered in Makkah and Madinah in the era of companions and, then, gradually the center of hadith narrators shifted towards Kufa, Baghdad and central Asia over a period of time. To the best of our knowledge, this the first comprehensive and systematic study based on SNA, representing the narrators as a social graph to analyze their contribution to the preservation and propagation of hadith.

E. V. Chesnokova

The article presents the main provisions of the sub-theory of standardization in the forensic expert activity, its place in the theoretical foundations of forensic expertology, and its relationship with other sub-theories of this science.The author defines the concepts of the subject, the object of the standardization sub-theory in forensic activity, highlights its specifics. The article emphasizes the favorable impact on the development of domestic standardization in forensic activities of foreign experience in the implementation of theoretical developments on standardization, including the formation of a hierarchy of its standards. It is shown that for the development of the sub-theory of standardization in forensic science, it is advisable to use the results of the introduction of standardization mechanisms in the practice of accredited forensic laboratories under the international standard GOST ISO/IEC 17025-2019 “General requirements for the competence of testing and calibration laboratories”, taking into account the theoretical foundations of forensic science.

Ranjan Pal, Junhui Li, Yixuan Wang et al.

This paper argues that data of strategic individuals with heterogeneous privacy valuations in a distributed online social network (e.g., Facebook) will be under-priced, if traded in a monopoly buyer setting, and will lead to diminishing utilitarian welfare. This result, for a certain family of online community data trading problems, is in stark contrast to a popular information economics intuition that increased amounts of end-user data signals in a data market improves its efficiency. Our proposed theory paves the way for a future (counter-intuitive) analysis of data trading oligopoly markets for online social networks (OSNs).

Jonas Tjomsland, Sinan Kalkan, Hatice Gunes

To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data, and (ii) the lack of formulations of this as a lifelong learning problem. In this paper, we address these two issues. We first introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB), which contains appropriateness labels of robot actions annotated by humans. To be able to control but vary the configurations of the scenes and the social settings, MANNERS-DB has been created utilising a simulation environment by uniformly sampling relevant contextual attributes. Secondly, we train and evaluate a baseline Bayesian Neural Network (BNN) that estimates social appropriateness of actions in the MANNERS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of the BNN parameters. The experimental results show that the social appropriateness of robot actions can be predicted with a satisfactory level of precision. Our work takes robots one step closer to a human-like understanding of (social) appropriateness of actions, with respect to the social context they operate in. To facilitate reproducibility and further progress in this area, the MANNERS-DB, the trained models and the relevant code will be made publicly available.