Claudia da Cruz Gomes, Adriano de Lemos Alves Peixoto
O objetivo deste estudo foi investigar a prevalência dos sintomas de transtornos mentais entre os Guardas Civis Municipais e analisar a relação entre os aspectos psicossociais do trabalho e o adoecimento psíquico. Trata-se de um estudo transversal, que utilizou como instrumentos a Depression, Anxiety and Stress Scale (DASS-21) e o Job Content Questionnaire (JCQ). Participaram 95 guardas. Os resultados indicaram baixa incidência de depressão (20%), ansiedade (19%) e estresse (19%) entre esses profissionais, e não há significância estatística nas comparações envolvendo os sintomas de transtornos mentais com os grupos controle sobre o trabalho, demandas físicas e suporte social. Conclui-se que, os níveis de adoecimento entre os guardas mostraram-se baixo, quando comparados com os índices apontados na literatura sobre essa temática faz-se necessário pensar nos problemas de saúde que acometem esses trabalhadores, para assim, minimizar e/ou evitar que este adoecimento sofra um aumento.
Social pathology. Social and public welfare. Criminology
In many real-world applications of reinforcement learning (RL), deployed policies have varied impacts on different stakeholders, creating challenges in reaching consensus on how to effectively aggregate their preferences. Generalized $p$-means form a widely used class of social welfare functions for this purpose, with broad applications in fair resource allocation, AI alignment, and decision-making. This class includes well-known welfare functions such as Egalitarian, Nash, and Utilitarian welfare. However, selecting the appropriate social welfare function is challenging for decision-makers, as the structure and outcomes of optimal policies can be highly sensitive to the choice of $p$. To address this challenge, we study the concept of an $α$-approximate portfolio in RL, a set of policies that are approximately optimal across the family of generalized $p$-means for all $p \in [-\infty, 1]$. We propose algorithms to compute such portfolios and provide theoretical guarantees on the trade-offs among approximation factor, portfolio size, and computational efficiency. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of our approach in summarizing the policy space induced by varying $p$ values, empowering decision-makers to navigate this landscape more effectively.
This research aims to identify the historical roots of Financial Statement Fraud (FSF) detection research and ascertain the trajectory of current and upcoming research in this field. This study conducted descriptive, reference spectroscopy, and scientific mapping analyses. To unearth the historical foundations of FSF detection research, the study employed the “Reference Publication Year Spectroscopy (RPYS)” technique. The study chose publications from 1989 to 2022 and identified a slow initial publication pace from 1989, followed by a surge in 2003, aligned with global accounting fraud scandals. Through RPYS, it identified 24 seminal research works (from 1881 to 2022) across multiple disciplines (mathematics, psychology, criminology, sociology, economics, finance, accounting, auditing, data analytics and machine learning) that contributed to the development of the FSF detection research. The trend of FSF research shifted from “financial reporting” to “machine learning”, which underscores the necessity for researchers, organizations, and policymakers to integrate emerging technologies like machine learning and data analytics and promote interdisciplinary and international cooperation to enhance the detection of FSF.
Social pathology. Social and public welfare. Criminology
The social networks of people experiencing homelessness are an understudied but vital aspect of their lives, offering access to information, support, and safety. In 2023, the U.S. Department of Housing and Urban Development reported 653,100 people experiencing homelessness on any given night -- a 23% rise since 2022, though likely an undercount. This paper examines a unique three-year dataset (2022-2024) of survey responses from over 3,000 unhoused individuals in King County, WA, collected via network-based sampling methods to estimate the unsheltered population. Our study analyzes the networks of the unsheltered population, focusing on acquaintance, close friendship, kinship, and peer referral networks. Findings reveal a decline in social connectivity over time. The average number of acquaintances dropped from 80 in 2023 to 40 in 2024. Close friendship levels remained stable at 2.5, but given the growth in the homeless population, this suggests decreased network connectivity. Kinship networks expanded, indicating that more family members of unhoused individuals are also experiencing homelessness. These trends suggest increasing social disconnection, possibly driven by displacement and a rise in newly homeless individuals. The growing isolation may reduce opportunities for information sharing and mutual support. However, the increased reliance on family networks highlights the shifting dynamics of social support within this community. This research underscores the need for policies fostering social connections and community building, such as reducing displacement and providing spaces for congregation, to counter the growing anomie among unhoused populations.
Abstract Law enforcement agencies struggle with criminals using end-to-end encryption (E2EE). A recent policy paper states: “while encryption is vital and privacy and cyber security must be protected, that should not come at the expense of wholly precluding law enforcement”. The main argument is that E2EE hampers attribution and prosecution of criminals who rely on encrypted communication - ranging from drug syndicates to child sexual abuse material (CSAM) platforms. This statement - in policy circles dubbed ‘going dark’ - is not yet supported by empirical evidence. That is why, in our work, we analyse public court data from the Netherlands to show to what extent law enforcement agencies and the public prosecution service are impacted by the use of E2EE in bringing cases to court and their outcome. Our results show that in cases brought to court, the Dutch courts appear to be as successful in convicting offenders who rely on E2EE as those who do not. Our data do not permit us to draw conclusions on the effect of E2EE on criminal investigations.
Science (General), Social pathology. Social and public welfare. Criminology
This paper explores the use of the Outcome Rating Scale (ORS) and the Session Rating Scale (SRS) in clinical social work supervision. These assessment tools are designed to provide clinicians with direct feedback from clients about the clients’ views on progress in therapy and their views on the quality of each session provided by the clinician. This type of feedback can be useful for beginning therapists as well as experienced therapists. This paper discusses helpful ways that these assessment tools can be used by clinical supervisors to increase the competence and self-awareness of clinical social workers in training. Supervisors can work with their trainees to help those in training better assess their own practice and what changes they might need to make based upon the results from the ORS and the SRS.
Social pathology. Social and public welfare. Criminology
La Antropología Forense aporta elementos que contribuyen para que las víctimas y sus familiares encuentren la verdad, la justicia, la memoria, la reparación y la reconciliación. El presente artículo tiene por objeto destacar la importancia de brindar soluciones a las familias de personas desaparecidas por causas delincuenciales, ambientales, migratorias, entre otras, y que no han recibido una justicia pronta. Supone un pequeño contexto sobre los avances que se han realizado en Honduras en la materia, y reflexiona sobre la importancia de crear con urgencia una ley de personas desaparecidas y un programa que incorpore la creación de los equipos de Antropología Forense y los apoyos internacionales necesarios para el reconocimiento de las víctimas.
Bradley Kievit, Jessica C. Xavier, Max Ferguson
et al.
Abstract Introduction Canada and the United States continue to experience increasing overdose deaths attributed to highly toxic illicit substances, driven by fentanyl and its analogues. Many bystanders report being hesitant to call 9-1-1 at an overdose due to fears around police presence and arrests. In Canada, a federal law was enacted in 2017, the Good Samaritan Drug Overdose Act (GSDOA), to provide protection from simple drug possession and related charges when 9-1-1 is called to an overdose. There is limited evidence, however, that the GSDOA has improved rates of intention to call 9-1-1 at overdose events. We therefore sought to examine intent to call 9-1-1 among persons who received GSDOA education and were at risk of witnessing an overdose. Methods A cross-sectional survey was conducted with people at risk of witnessing an overdose recruited at 19 Take Home Naloxone (THN) program sites across British Columbia as well as online through Foundry from October 2020 to April 2021. Descriptive statistics were used to examine intention to call 9-1-1 at future overdoses. Multivariable logistic regression models were built in hierarchical fashion to examine factors associated with intention to call 9-1-1. Results Overall, 89.6% (n = 404) of the eligible sample reported intention to call 9-1-1. In the multivariable model, factors positively associated with intention to call 9-1-1 included identifying as a cisgender woman (adjusted odds ratio [AOR]: 3.37; 95% CI: 1.19–9.50) and having previous GSDOA awareness ([AOR]: 4.16; 95% CI: 1.62–10.70). Having experienced a stimulant overdose in the past 6 months was negatively associated with intention to call 9-1-1 ([AOR]: 0.24; 95% CI: 0.09–0.65). Conclusion A small proportion of the respondents reported that, despite the enactment of GSDOA, they did not intend to call 9-1-1 and those who were aware of the act were more likely to report an intention to call at future overdose events. Increasing GSDOA awareness and/or additional interventions to support the aims of the GSDOA could address ongoing reluctance to seek emergency medical care by people who use drugs.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and illformed data becomes challenging. To address these challenges, events are extracted from streaming social media data in the form of keyphrases using different cognitive properties. The motivation behind this research work is to provide substantial improvements in the lexical variation of event phrases while detecting events and sub-events from twitter data. In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
This article discusses public social welfare provision to homeless EU migrants in Norway. Most of these migrants have no or weak affiliations with the formal labour market, resulting in restricted rights to public social assistance. Drawing on the concept of precarious inclusion, I suggest that rather than being simply excluded from public social welfare, homeless EU migrants are included in the welfare state but in fragile and insecure ways through provisions directed at safeguarding bodily survival. I understand these limited inclusionary policies and practices as forming part of the Norwegian state’s management of ‘undesired’ migrants. Building on interviews with social workers in the public social welfare administration, I reflect on how assessments of cases involving homeless EU migrants signal hierarchical conceptions and differentiation of human worth within Norway’s borders and how territorial belonging emerges as a prerequisite for ‘deservingness’ in social workers’ accounts.
Cristina Sobrino Garcés , Marta Murrià Sangenís , Carlos González Murciano
Las encuestas de victimización constituyen una fuente de información relevante para la investigación criminológica. En Cataluña hay una larga tradición en este tipo de encuestas y la Encuesta de Victimización del Área Metropolitana de Barcelona (EVAMB) es especialmente útil para el análisis de la seguridad desde una perspectiva urbana y metropolitana. En lo que se refiere a la cuantificación de la actividad delictiva y de la victimización producida por la delincuencia convencional, esta encuesta es pionera en geolocalizar el lugar de residencia de las víctimas y el lugar donde ocurren las victimizaciones. El artículo expone la trayectoria de las encuestas de victimización en Cataluña, centrándose en la EVAMB y describiendo sus principales características. A su vez, con la idea de mostrar su potencialidad, se presenta un ejemplo de análisis que tiene por objetivo determinar la relación entre la distribución espacial de la delincuencia y los patrones de movilidad de las víctimas en el área metropolitana de Barcelona. Los resultados señalan la importancia de tener en cuenta la movilidad de las víctimas para comprender mejor el riesgo de victimización en los distintos territorios. Este trabajo contribuye a comprender como las encuestas de victimización son una buena fuente datos tanto para el análisis territorial del delito y la victimización como para informar a las políticas públicas de seguridad y prevención.
Social pathology. Social and public welfare. Criminology, Social Sciences
The Ubiquitous nature of smartphones has significantly increased the use of social media platforms, such as Facebook, Twitter, TikTok, and LinkedIn, etc., among the public, government, and businesses. Facebook generated ~70 billion USD in 2019 in advertisement revenues alone, a ~27% increase from the previous year. Social media has also played a strong role in outbreaks of social protests responsible for political changes in different countries. As we can see from the above examples, social media plays a big role in business intelligence and international politics. In this paper, we present and discuss a high-level functional intelligence model (recipe) of Social Media Analysis (SMA). This model synthesizes the input data and uses operational intelligence to provide actionable recommendations. In addition, it also matches the synthesized function of the experiences and learning gained from the environment. The SMA model presented is independent of the application domain, and can be applied to different domains, such as Education, Healthcare and Government, etc. Finally, we also present some of the challenges faced by SMA and how the SMA model presented in this paper solves them.
Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu
et al.
The concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment's settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also list an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.
The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, seeking to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Can the changing pandemic awareness predict the Covid-19 outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track the awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more significant disparities in awareness than European countries, and awareness in the eastern part of Europe was higher than in central Europe. Finally, the Ratio index could accurately predict global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-30, 35-42, and 17 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research.
Shu-Feng Tsao, Helen Chen, Therese Tisseverasinghe
et al.
With the onset of COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak starting in November 2019 until May 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social platforms and COVID-19. These themes focused on: (i) surveying public attitudes, (ii) identifying infodemics, (iii) assessing mental health, (iv) detecting or predicting COVID-19 cases, (v) analyzing government responses to the pandemic, and (vi) evaluating quality of health information in prevention education videos. Furthermore, our review highlights the paucity of studies on the application of machine learning on social media data related to COVID-19 and a lack of studies documenting real-time surveillance developed with social media data on COVID-19. For COVID-19, social media can play a crucial role in disseminating health information as well as tackling infodemics and misinformation.
Gustavo Penacino, Facundo Zapata, Ixchel De la Luz Martínez
et al.
Desde hace 15 años la Sociedad Latinoamericana de Genética Forense realiza un ejercicio de calidad, comparativo interlaboratorios teórico y práctico, cuyo objetivo principal es contribuir al fortalecimiento de los laboratorios de genética forense participantes. Se analizó el registro de resultados publicados en el portal web de la sociedad (http://www.slagf.org), encontrándose que el error promedio cuando se analizan STRs autosómicos es del 2,01% y de 2,28% para STRs del Cromosoma Y, a lo largo de estos años, también se observó una evolución de la metodología analítica utilizada, lo que se refleja en el número de errores detectados. Se requiere realizar un proceso de reorganización de los ejercicios de calidad para seguir contribuyendo al fortalecimiento de los laboratorios dedicados a la genética forense.
In recent years, social media has become a ubiquitous and integral part of social networking. One of the major attentions made by social researchers is the tendency of like-minded people to interact with one another in social groups, a concept which is known as Homophily. The study of homophily can provide eminent insights into the flow of information and behaviors within a society and this has been extremely useful in analyzing the formations of online communities. In this paper, we review and survey the effect of homophily in social networks and summarize the state of art methods that has been proposed in the past years to identify and measure the effect of homophily in multiple types of social networks and we conclude with a critical discussion of open challenges and directions for future research.
This paper studies the feasibility of synthetic data generation for mission-critical applications. The emphasis is on synthetic data generation for anomalous detection in complex social networks. In particular, the development of a heuristic generative model, capable of creating data for anomalous rare activities in complex social networks is sought. To this end, lessons from social and political literature are applied to prototype a novel implementation of the Agent-based Modeling (ABM) framework, based on simple social interactions between agents, for synthetic data generation in the context of terrorist profile desegregation. The conclusion offers directions for further verification, fine-tuning, and proposes future directions of work for the ABM prototype, as a complex-societal approach to synthetic data generation, by identifying heuristic hyper-parameter tuning methodologies to further ensure the generated data distribution is similar to the true distribution of the original data-sets. While a rigorous mathematical optimization for reducing the distances in distributions is not offered in this work, we opine that this prototype of an autonomous-agent generative complex social model is useful for studying and researching on pattern of life and anomaly detection where there is strict limitation or lack of sufficient data for using practical machine learning solutions in mission-critical applications.
In addition to seamless connectivity and smartness, the objects in the Internet of Things (IoT) are expected to have the social capabilities -- these objects are termed as ``social objects''. In this paper, an intuitive paradigm of social interactions between these objects are argued and modeled. The impact of social behavior on the interaction pattern of social objects is studied taking Peer-to-Peer (P2P) resource sharing as an example application. The model proposed in this paper studies the implications of competitive vs. cooperative social paradigm, while peers attempt to attain the shared resources / services. The simulation results divulge that the social capabilities of the peers impart a significant increase in the quality of interactions between social objects. Through an agent-based simulation study, it is proved that cooperative strategy is more efficient than competitive strategy. Moreover, cooperation with an underpinning on real-life networking structure and mobility does not negatively impact the efficiency of the system at all; rather it helps.