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

Flavia Cirimele, Carla Zappulla, Roberta T. Di Rosa

Introduction. The increasing presence of cultural diversity has become a prevalent aspect of social work in many EU countries. Although public services represent a relevant source of support for the' inclusion process of immigrants, they also face several challenges in providing tailored and suitable health care to accommodate cultural variation and meet the diverse cross-cultural needs of immigrants. In this context, social work with immigrants is grounded in the principles of empathy and intercultural sensitivity. Social workers should value diversity and provide services that reflect an understanding of the diversity across cultures. However, given the heavy demands placed on them, coupled with increasingly diminishing resources, they often may experience psychological distress. Methods. Building on a multidisciplinary foundation, this study focuses on understanding the well-being condition of professionals operating in the migration sector by evidencing whether the levels of burnout (i.e., the experience of exhaustion, cynicism, and inefficiency experienced at work because of chronic emotional and interpersonal stressor on the job) are associated with individuals’ empathy and sensitivity to different cultures and background. In detail, participants were 38 social workers, aged between 30 and 60 years (M = 39.63, SD = 11.22), from Italy (N = 8), Spain (N = 23), and Sweden (N = 7). Participants responded using well-validated instruments across the three countries for assessing burnout, empathy, and intercultural sensitivity. Results. The analysis of variance revealed significant differences among social workers in Italy, Spain, and Sweden in terms of mental distance at work, interaction attentiveness when communicating with people from different backgrounds, and empathy. Beyond these differences, the findings of the profile analysis show that social workers with higher intercultural sensitivity and average levels of empathy generally report lower burnout symptoms across contexts. Discussion and conclusions. These results suggest that cultural sensitivity may be a protective factor against work-related stress across contexts, emphasizing the importance of fostering intercultural sensitivity through targeted training and support to enhance social workers' well-being and improve service delivery to migrant populations. The present study extends knowledge on the multifaceted mosaic of the everyday relationship between social workers and migration, promoting a greater understanding of professional perspectives from a multicultural perspective and the exchange of good practices between different territories and services.  

Maxence Morin, Baptiste Hemery, Fabrice Jeanne et al.

Social network analysis is pivotal for organizations aiming to leverage the vast amounts of data generated from user interactions on social media and other digital platforms. These interactions often reveal complex social structures, such as tightly-knit groups based on common interests, which are crucial for enhancing service personalization or fraud detection. Traditional methods like community detection and graph matching, while useful, often fall short of accurately identifying specific groups of users. This paper introduces a novel framework specifically designed to identify groups of users within transactional graphs by focusing on the contextual and structural nuances that define these groups.

Qiao Wang, Liang Liu, Mitsuo Yoshida

Social video platforms shape how people access information, while recommendation systems can narrow exposure and increase the risk of toxic interaction. Previous research has often examined text or users in isolation, overlooking the structural context in which such toxic interactions occur. Without considering who interacts with whom and around what content, it is difficult to explain why negative expressions cluster within particular communities. To address this issue, this study focuses on the Chinese social video platform Bilibili, incorporating video-level information as the environment for user expression, modeling users and videos in an interaction matrix. After normalization and dimensionality reduction, we perform separate clustering on both sides of the video-user interaction matrix with K-means. Cluster assignments facilitate comparisons of user behavior, including message length, posting frequency, and source (barrage and comment), as well as textual features such as sentiment and toxicity, and video attributes defined by uploaders. Such a clustering approach integrates structural ties with content signals to identify stable groups of videos and users. We find clear stratification in interaction style (message length, comment ratio) across user clusters, while sentiment and toxicity differences are weak or inconsistent across video clusters. Across video clusters, viewing volume exhibits a clear hierarchy, with higher exposure groups concentrating more toxic expressions. For such a group, platforms should require timely intervention during periods of rapid growth. Across user clusters, comment ratio and message length form distinct hierarchies, and several clusters with longer and comment-oriented messages exhibit lower toxicity. For such groups, platforms should strengthen mechanisms that sustain rational dialogue and encourage engagement across topics.

Katarzyna Linkowska

This paper presents the results of genetic testing in two cases of shared paternity between half-siblings. In the first example, the research was based on the analysis of half-siblings and their mothers with respect to 21 autosomal markers. In the second, the research was based on both the analysis of 30 autosomal markers and X chromosome markers in putative sisters and the mother of one of them. Selected examples are presented to illustrate how the use of different research strategies affects the outcome of the likelihood ratio and the ability to make inferences in complex kinship cases.

Stephen Scarano, Vijayalakshmi Vasudevan, Mattia Samory et al.

Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 2020 presidential elections in the U.S., this study shows that outcomes of election polls on Twitter deviate from election results despite their prevalence. Leveraging demographic inference and statistical analysis, we find that Twitter polls are disproportionately authored by older males and exhibit a large bias towards candidate Donald Trump relative to representative mainstream polls. We investigate potential sources of biased outcomes from the point of view of inauthentic, automated, and counter-normative behavior. Using social media experiments and interviews with poll authors, we identify inconsistencies between public vote counts and those privately visible to poll authors, with the gap potentially attributable to purchased votes. We also find that Twitter accounts participating in election polls are more likely to be bots, and election poll outcomes tend to be more biased, before the election day than after. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple thousand of such polls were posted in 2020. The study discusses the implications of biased election polls in the context of transparency and accountability of social media platforms.

Simindokht Jahangard, Zhixi Cai, Shiki Wen et al.

Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short, necessitating a comprehensive approach that considers individual behaviour, intra-group dynamics, and social group levels for a thorough understanding. To address dataset limitations, this paper introduces JRDB-Social, an extension of JRDB. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts, JRDB-Social provides annotations at three levels: individual attributes, intra-group interactions, and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models, we evaluated our benchmark to explore their capacity to decipher social human behaviour.

Rachel P. Winograd, Bridget Coffey, Melissa Nance et al.

Opioid-involved overdose deaths continue to climb, in part because of suboptimal access to and retention on medications for opioid use disorder (MOUD), including buprenorphine. Low barrier buprenorphine treatment aims to reduce or eliminate the threshold for getting and staying on medication by providing immediate and long-term access to buprenorphine without strict rules or requirements. This study examines associations between medical providers’ beliefs about treating people with opioid use disorder (OUD) and naloxone access with their self-reported low-barrier buprenorphine prescribing practices. We surveyed and analyzed responses from providers (N = 86) who completed X-waiver courses in Missouri between March 2017 and September 2019, of which 55% (n = 47) both completed the full survey and endorsed prescribing buprenorphine since the training. The survey included questions about buprenorphine prescribing behaviors as well as the Naloxone-Related Risk Compensation Beliefs (NaRCC-B) scale and the Attitudes toward Patients with OUD scale. Analyses consisted of a series of linear and logistic regressions with the NaRCC-B and OUD Attitudes scales predicting various domains of low-barrier prescribing behaviors. Findings indicate medical providers’ beliefs about treating people with OUD are associated with their practice of addiction medicine, with individuals with more favorable views being more likely to endorse low-barrier buprenorphine prescribing practices including offering telemedicine and at-home inductions, prescribing higher doses of buprenorphine, treating larger caseloads, and discussing overdose risk and protective factors with their patients. Providers’ beliefs about naloxone being enabling were less related to their buprenorphine practices but strongly related to their likelihood of providing naloxone. Future research may examine which strategies effectively change prescriber attitudes and their adoption of lower-barrier prescribing practices.

Florencia Rubiolo, Gonzalo Fiore-Viani

El debate en torno al rol de China en el orden internacional actual continúa partiendo las aguas dentro de las discusiones teóricas de las Relaciones Internacionales. ¿Está Beijing buscando establecer un nuevo orden? ¿O, partiendo de la relativa debilidad del orden liberal internacional, está redefiniendo algunos principios, pero aun así contribuye a sostenerlo?  El objetivo del artículo es analizar el rol de la República Popular China en el orden internacional y las implicancias que puede tener en la definición de su rol en el orden regional de América del Sur. El abordaje metodológico es de naturaleza cualitativa. Se parte de la revisión de fuentes secundarias para construir la discusión teórica central. Luego se utilizan fuentes primarias estadísticas y secundarias bibliográficas y documentales, para analizar tres casos específicos: Argentina, Brasil y Chile. Se concluye que el rol de China a escala global, y regional en América Latina, debe comprenderse aún dentro del orden internacional actual, en el cual cumple una función de sostenimiento, particularmente mediante la gobernanza global multilateral.   Abstract The debate around the role of China in the current international order continues to split the waters within the theoretical discussions of International Relations. Is Beijing seeking to establish a new order? Or, starting from the relative weakness of the international liberal order, is it redefining some principles, but still helping to sustain it? The main objective of the article is to analyze the role of the People's Republic of China in the international order, and the implications that it may have in the definition of its role in the regional order of South America. The methodological approach is qualitative, with an in-depth review of secondary sources to construct the central theoretical discussion, and then using primary statistical and secondary bibliographic and documentary sources for the analysis of three specific cases: Argentina, Brazil and Chile. It is concluded that the role of China at a global level and in Latin America must still be understood within the current international order, where it plays a role in sustaining it, particularly from the multilateral global governance perspective.

Liam Hebert, Gaurav Sahu, Yuxuan Guo et al.

We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the discussion surrounding a comment and grounding the interwoven fusion layers that combine text and image embeddings instead of processing modalities separately. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies.

Lukas Andreas Basedow, Melina Felicitas Wiedmann, Veit Roessner et al.

Abstract Background Post-traumatic stress disorder (PTSD) and substance use disorders (SUDs) often co-occur in adolescent patients. Previous research has shown that these patients differ from SUD patients without PTSD in terms of their substance use patterns. In this study, we aimed to test whether substance use in this population is related to an attempt to self-medicate PTSD-related symptoms. Methods German adolescent patients (aged 13–18 years) at an outpatient clinic for SUD treatment, n = 111 (43% female), completed a self-designed questionnaire on use motives, a measure of PTSD-related experiences, and underwent a standardized psychiatric interview including structured substance use questions. Participants were subsequently classified as ‘no traumatic experiences (‘noTEs’ but SUD), ‘traumatic experiences but no current PTSD diagnosis’ (‘TEs’ with SUD), and ‘PTSD’ with SUD. After establishing a self-designed motive measurement through exploratory and confirmatory factor analyses, we calculated non-parametric group differences and a mediation analysis in a linear regression framework. Results The past-year frequency of MDMA use was highest in the PTSD group and lowest in the noTE group (H (2) = 7.2, p = .027, η 2  = .058), but no differences were found for frequencies of tobacco, alcohol, cannabis, or stimulant use (all H ≤ 4.9, p ≥ .085, η 2  ≤ .033). While controlling for sex, the three groups showed a similar pattern (highest in the PTSD group and lowest in the noTE group) for coping scores (F (103) = 5.77, p = .004, η 2  = .101). Finally, mediation analyses revealed an indirect effect of coping score (b = 0.61, 95% CI [0.29, 1.58], p = .145) on the association between group membership and MDMA use frequency. Conclusions In adolescent SUD patients, we found an association of current PTSD and lifetime traumatic experiences with higher MDMA use that could be partially explained by substance use being motivated by an attempt to cope with mental health symptoms. This indicates a coping process involved specifically in MDMA use compared to the use of other psychoactive substances, possibly due to unique psychoactive effects of MDMA.

Roberto Catello

Two of the most promising developments to emerge from the failed attempts to contain the spread of infectious disease outbreaks since the World Health Organization (WHO) declared the outbreak of novel coronavirus (2019-nCoV) a pandemic on 23 January 2020 are (i) an acceleration of the critique of neoliberalism and (ii) a reinvigoration of the state crime vocabulary. As Henry Giroux (2020) argued, the pandemic has exposed “the plague of neoliberalism” and revealed the need for a new language to make sense of the current crisis. In this article, I argue that the language of state crime has both the terminological and the conceptual capacity to inform such a sense-making endeavour and, relatedly, that the academic literature on state criminality can complement critiques of the global neoliberal order in the context of the failed governance of the COVID-19 pandemic.

Dallas Augustine, Melissa Barragan, Kelsie Chesnut et al.

Anne M. Goodall, Alexis H. Truong

In this study we explore how participation in tabletop role-playing games (TTRPGs) fosters experiences of social insertion in adolescence and adulthood. We conducted semi-directed interviews with nine participants who identified themselves as having used such practices to overcome difficult and challenging life experiences. We look at how participants interpreted their play experiences, described by some as “therapeutic.” Through TTRPGs, players were able to explore and better understand aspects of themselves, explore new interactions, and “test” new ways of expressing themselves. Additionally, participants were able to develop their interpersonal skills by participating in such practices, namely because of the roleplaying element. Participants stated that these practices and their therapeutic qualities also had positive effects on their lives outside of the game, helping them to enter and engage in various social situations that they previously felt excluded from, or hesitant to participate in. We argue that social interventions could invest in these types of cultural practices, embedded in popular culture, to encourage and facilitate participation of adolescents and adults in mental health services.  

Mohit Singhal, Nihal Kumarswamy, Shreyasi Kinhekar et al.

Prior work has extensively studied misinformation related to news, politics, and health, however, misinformation can also be about technological topics. While less controversial, such misinformation can severely impact companies' reputations and revenues, and users' online experiences. Recently, social media has also been increasingly used as a novel source of knowledgebase for extracting timely and relevant security threats, which are fed to the threat intelligence systems for better performance. However, with possible campaigns spreading false security threats, these systems can become vulnerable to poisoning attacks. In this work, we proposed novel approaches for detecting misinformation about cybersecurity and privacy threats on social media, focusing on two topics with different types of misinformation: phishing websites and Zoom's security & privacy threats. We developed a framework for detecting inaccurate phishing claims on Twitter. Using this framework, we could label about 9% of URLs and 22% of phishing reports as misinformation. We also proposed another framework for detecting misinformation related to Zoom's security and privacy threats on multiple platforms. Our classifiers showed great performance with more than 98% accuracy. Employing these classifiers on the posts from Facebook, Instagram, Reddit, and Twitter, we found respectively that about 18%, 3%, 4%, and 3% of posts were misinformation. In addition, we studied the characteristics of misinformation posts, their authors, and their timelines, which helped us identify campaigns.

Max Aliapoulios, Emmi Bevensee, Jeremy Blackburn et al.

Parler is as an "alternative" social network promoting itself as a service that allows to "speak freely and express yourself openly, without fear of being deplatformed for your views." Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.

Mustafa Toprak, Chiara Boldrini, Andrea Passarella et al.

Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging social media information to grow their business. Predictions are typically based on unsupervised or supervised learning, often leveraging simple yet effective graph topological information, such as the number of common neighbors. However, we argue that richer information about personal social structure of individuals might lead to better predictions. In this paper, we propose to leverage well-established social cognitive theories to improve link prediction performance. According to these theories, individuals arrange their social relationships along, on average, five concentric circles of decreasing intimacy. We postulate that relationships in different circles have different importance in predicting new links. In order to validate this claim, we focus on popular feature-extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social-circles awareness. We validate the prediction performance of these circle-aware algorithms against several benchmarks (including their baseline versions as well as node-embedding- and GNN-based link prediction), leveraging two Twitter datasets comprising a community of video gamers and generic users. We show that social-awareness generally provides significant improvements in the prediction performance, beating also state-of-the-art solutions like node2vec and SEAL, and without increasing the computational complexity. Finally, we show that social-awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.

Jesús Glay Mejía-Naranjo

Mirka Snyder Caron, Abhishek Gupta

Like any technology, AI systems come with inherent risks and potential benefits. It comes with potential disruption of established norms and methods of work, societal impacts and externalities. One may think of the adoption of technology as a form of social contract, which may evolve or fluctuate in time, scale, and impact. It is important to keep in mind that for AI, meeting the expectations of this social contract is critical, because recklessly driving the adoption and implementation of unsafe, irresponsible, or unethical AI systems may trigger serious backlash against industry and academia involved which could take decades to resolve, if not actually seriously harm society. For the purpose of this paper, we consider that a social contract arises when there is sufficient consensus within society to adopt and implement this new technology. As such, to enable a social contract to arise for the adoption and implementation of AI, developing: 1) A socially accepted purpose, through 2) A safe and responsible method, with 3) A socially aware level of risk involved, for 4) A socially beneficial outcome, is key.

von Lampe Klaus

Srisombat Chokprajakchat, Wanaporn Techagaisiyavanit

Thailand has one of the highest incarceration rates of women in the world. With an increasing prison population overall as well as an increasing proportion of female inmates, the country faces one of its most challenging tasks in penitentiary administration: reforms to its legal landscape and its correctional practices in line with international standards. A response to such a crisis is to undertake a prison evaluation project to ensure proper implementation of the United Nations Rules for the Treatment of Women Prisoners and Non-custodial Measures for Women Offenders (also known as the ‘Bangkok Rules’). The primary objective of this research article is to assess and identify a prison model that can inspire the development of other prison facilities, while supporting a firm commitment to maintain and improve the status of current model facilities.