Social media platforms connect billions, but their business models often amplify societal harm through misinformation, which is linked to polarization, violence, and declining mental health. Current governance frameworks, such as the U.S. Section 230 and the EU Digital Services Act, delegate content moderation to corporations. This creates structural conflicts of interest because misinformation drives engagement, and engagement drives profit. We propose a public utility model for social media governance that prioritizes the public good over commercial incentives. Integrating legislated content removal with democratic content moderation, the model protects free expression while mitigating societal harms. It frames social media as sovereign digital infrastructure governed through democratic oversight, transparent algorithms, and institutional safeguards.
We develop an axiomatic framework to evaluate income distributions from the perspective of an opportunity-egalitarian social planner. Building on a formal link with the literature on decision theory under ambiguity, we characterize a class of opportunity-sensitive social welfare functions based on a two-stage evaluation: the planner first computes the expected utility of income within each social type, where types consist of individuals sharing the same circumstances beyond their control, and then aggregates these type-specific welfare levels through a transformation reflecting aversion to inequality of opportunity. The evaluation is governed by a single parameter. We provide equivalent representations of the social welfare function, including a mean-divergence form that separates an efficiency term from an inequality term, and we establish an opportunity stochastic dominance criterion. Finally, we derive inequality measures that decompose overall inequality into within-group risk and between-group inequality of opportunity, providing a tractable basis for normative welfare analysis.
Abstract
Social workers are essential in human service organizations as they play an important role in the implementation of programs and in addressing the needs of diverse populations. However, their well-being and performance can suffer due to the stress and challenges inherent in their work environments. This paper aims to address the urgent need to respond to social workers’ self-care needs by exploring the potential of a culturally adapted mindfulness-based intervention in addressing issues such as stress and depression among social workers in the Philippines. Through a review of relevant literature, this paper examined mindfulness-based intervention as an emerging self-care practice and approach to addressing work-related health issues. It proposed the development and assessment of the Stress Management for Improved Living and Empowerment (SMILE) program, which is a culturally tailored intervention designed to meet the unique needs of social workers. Pending successful evaluation of its feasibility, efficacy, and effectiveness, social welfare and human service organizations may implement the proposed intervention to promote self-care and empowerment among social workers.
Social pathology. Social and public welfare. Criminology
James M. Berzuk, Lauren Corcoran, Brannen McKenzie-Lefurgey
et al.
Contemporary robots are increasingly mimicking human social behaviours to facilitate interaction, such as smiling to signal approachability, or hesitating before taking an action to allow people time to react. Such techniques can activate a person's entrenched social instincts, triggering emotional responses as though they are interacting with a fellow human, and can prompt them to treat a robot as if it truly possesses the underlying life-like processes it outwardly presents, raising significant ethical questions. We engage these issues through the lens of informed consent: drawing upon prevailing legal principles and ethics, we examine how social robots can influence user behaviour in novel ways, and whether under those circumstances users can be appropriately informed to consent to these heightened interactions. We explore the complex circumstances of human-robot interaction and highlight how it differs from more familiar interaction contexts, and we apply legal principles relating to informed consent to social robots in order to reconceptualize the current ethical debates surrounding the field. From this investigation, we synthesize design goals for robot developers to achieve more ethical and informed human-robot interaction.
Susanna Aba Abraham, Francis Annor, Obed Cudjoe
et al.
Abstract Background The World Health Organization (WHO) has indicated that the absence of prison health poses a threat to public health, making it important to safeguard access to quality healthcare for incarcerated populations. Although several studies have explored the quality of care in prisons, there is a dearth of empirical evidence on the perspectives of incarcerated individuals. This study investigated incarcerated individuals’ perspectives and opinions on the general healthcare services in Ghanaian prisons. Methods Utilizing a qualitative approach, focus group discussions were conducted with 51 incarcerated individuals in five prisons sited in the Northern, Middle and Southern zones of Ghana. Thematic analysis following the tradition of Braun and Clarke was conducted. Four of the six constructs of the WHO Health Systems Framework – service delivery, health workforce, access to essential medicines, and leadership and guidance – were applied deductively to organise the data into themes and subthemes. Results Four themes were generated from the analysis: “Health service delivery”, “Health workforce in prisons”, “Access to essential medicines” and “Leadership; regulating healthcare services”. Participants rated health services in prisons as below average compared to those available to the general population. The use of nurses as prescribers in prison infirmaries, though consistent with Ghana Health Service policy, seems to negatively influence prisoners’ perceptions of the quality of the health workforce in prisons. Lack of basic equipment and essential medications at the infirmary for common endemic conditions such as malaria coupled with the bureaucratic processes required to access care outside of the prison also negatively affected incarcerated individuals’ perceptions of the quality of health care. Conclusions Incarcerated individuals perceived that the quality of health services provided in prisons was inferior to that provided in the general population. Addressing challenges associated with the unavailability of essential drugs and equipment, improving the number of health staff, and addressing bottlenecks in accessing urgent care will enhance the experiences of incarcerated populations on the quality of care given.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
O presente estudo possui como temática o confisco alargado, mecanismo que, a partir da introdução do artigo 91-A ao Código Penal pela Lei 13.964/19, passou a figurar como novo efeito extrapenal da condenação. O dispositivo autoriza, em determinados casos, o perdimento de bens incompatíveis com a renda lícita do condenado, mesmo que desvinculados da conduta criminosa concretamente considerada. A análise é voltada especificamente à exposição de motivos do chamado “Pacote Anticrime”, a fim de demonstrar a incongruência do texto legal, não apenas com os princípios constitucionais penais, mas também com as próprias justificativas apresentadas, à época, para sua implementação.
Criminal law and procedure, Social pathology. Social and public welfare. Criminology
In this position paper, we outline our research challenges in Affective Interactive Systems, and present recent work on visualizing avatar biosignals for social VR entertainment. We highlight considerations for how biosignals animations in social VR spaces can (falsely) indicate users' availability status.
In today's fast-paced world, the rates of stress and depression present a surge. Social media provide assistance for the early detection of mental health conditions. Existing methods mainly introduce feature extraction approaches and train shallow machine learning classifiers. Other researches use deep neural networks or transformers. Despite the fact that transformer-based models achieve noticeable improvements, they cannot often capture rich factual knowledge. Although there have been proposed a number of studies aiming to enhance the pretrained transformer-based models with extra information or additional modalities, no prior work has exploited these modifications for detecting stress and depression through social media. In addition, although the reliability of a machine learning model's confidence in its predictions is critical for high-risk applications, there is no prior work taken into consideration the model calibration. To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra linguistic information in transformer-based models, namely BERT and MentalBERT. Specifically, the proposed approach employs a Multimodal Adaptation Gate for creating the combined embeddings, which are given as input to a BERT (or MentalBERT) model. For taking into account the model calibration, we apply label smoothing. We test our proposed approaches in three publicly available datasets and demonstrate that the integration of linguistic features into transformer-based models presents a surge in the performance. Also, the usage of label smoothing contributes to both the improvement of the model's performance and the calibration of the model. We finally perform a linguistic analysis of the posts and show differences in language between stressful and non-stressful texts, as well as depressive and non-depressive posts.
Vishnuprasad Padinjaredath Suresh, Gianluca Nogara, Felipe Cardoso
et al.
The aftermath of the 2020 US Presidential Election witnessed an unprecedented attack on the democratic values of the country through the violent insurrection at Capitol Hill on January 6th, 2021. The attack was fueled by the proliferation of conspiracy theories and misleading claims about the integrity of the election pushed by political elites and fringe communities on social media. In this study, we explore the evolution of fringe content and conspiracy theories on Twitter in the seven months leading up to the Capitol attack. We examine the suspicious coordinated activity carried out by users sharing fringe content, finding evidence of common adversarial manipulation techniques ranging from targeted amplification to manufactured consensus. Further, we map out the temporal evolution of, and the relationship between, fringe and conspiracy theories, which eventually coalesced into the rhetoric of a stolen election, with the hashtag #stopthesteal, alongside QAnon-related narratives. Our findings further highlight how social media platforms offer fertile ground for the widespread proliferation of conspiracies during major societal events, which can potentially lead to offline coordinated actions and organized violence.
"Grand corruption" and "state capture" are two intertwined concepts of corruption that have become systemic and institutionalized in many transitional countries around the world. "State capture" can simply be defined as "the payment of bribes at high levels of government in order to extract or plunder significant amounts of money from the state". The following paper will argue that when state capture occurs in transitional countries, it runs the risk of becoming socially embedded and institutionalized, which in turn makes it difficult to maintain the principles of democracy and threatens the overall stability of a country in transition. South Africa makes for a useful case study because it clearly represents how corruption in the form of state capture has infiltrated the political landscape of a country in transition, thereby rendering all state institutions redundant and threatening the principles of democracy. The paper will research what the dangers of state capture means for the countries in transition with the aim of proposing recommendations of minimizing state capture in order to reduce the negative consequences for security, peace and democracy. One corruption scandal that occurred in South Africa will be described which became known as "state capture". The paper was prepared based on the analysis of documents, academic and media articles that focus on state capture and the corruption in transitional countries. The paper will conclude that governmental corruption has become socially embedded in the "logics" of negotiation and interaction, thereby indicating that it has become institutionalized and culturally embedded within South Africa.
Social pathology. Social and public welfare. Criminology
Line Tegner Stelander, Anne Høye, Jørgen G. Bramness
et al.
Abstract Background As the population of older adults continues to grow, changes in alcohol consumption are important to monitor because an increase may have public health consequences. Rates of alcohol use vary with geographical location. The aim of this study was to examine trends in alcohol consumption among older adults in a geographically defined area in Norway, especially changing sex differences in drinking patterns over a 22-year period. Methods Repeated cross-sectional survey (in 1994–95, 2007–08, and 2015–16) of a general population of older adults. Eligible for this study were 20,939 participants (aged 60–99 years). The data were analysed using generalized estimating equations, stratified by age and sex. Alcohol consumption and drinking patterns were assessed, using an adaptation of the AUDIT-C. Results Between 1994 and 2016, there has been a significant increase in the proportion of current drinkers among older adults. Furthermore, the probability of frequent drinking (alcohol consumption at least twice weekly) increased significantly between 1994 and 2016, particularly among older women; OR 8.02 (CI 5.97–10.79) and OR 5.87 (CI 4.00–8.63) in the age groups 60–69 and 70+ respectively for women, and OR 4.13 (CI 3.42–4.99) and OR 3.10 (CI 2.41–3.99), in the age groups 60–69 and 70+ respectively for men. The majority of older adults drank small amounts of alcohol on typical drinking days, but there was an increasing probability of drinking three drinks or more on each occasion over the study period, except among women aged 70+ years. Conclusions Among older adults in Norway, alcohol consumption in terms of frequency and quantity on typical drinking days has increased considerably from 1996 to 2016. This change is in the opposite direction of what has been reported among younger adults. The gap between women and men in frequent drinking has been markedly narrowed, which indicate that women’s drinking patterns are approaching those of men. This may involve a need to change alcohol policy in Norway to more targeted interventions aimed at older people.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small groups responsible for instigating or sustaining them. To reveal latent (i.e., hidden) networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based strategies, a number of which we describe. The approach relies upon a pipeline that extracts relevant elements from social media posts, infers connections between accounts based on criteria matching the coordination strategies to build an undirected weighted network of accounts, which is then mined for communities exhibiting high levels of evidence of coordination using a novel community extraction method. We address the temporal aspect of the data by using a windowing mechanism, which may be suitable for near real-time application. We further highlight consistent coordination with a sliding frame across multiple windows and application of a decay factor. Our approach is compared with other recent similar processing approaches and community detection methods and is validated against two relevant datasets with ground truth data, using content, temporal, and network analyses, as well as with the design, training and application of three one-class classifiers built using the ground truth; its utility is furthermore demonstrated in two case studies of contentious online discussions.
We propose social welfare optimization as a general paradigm for formalizing fairness in AI systems. We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology. Rather than attempting to reduce bias between selected groups, one can achieve equity across all groups by incorporating fairness into the social welfare function. This also allows a fuller accounting of the welfare of the individuals involved. We show how to integrate social welfare optimization with both rule-based AI and machine learning, using either an in-processing or a post-processing approach. We present empirical results from a case study as a preliminary examination of the validity and potential of these integration strategies.
When an individual's behavior has rational characteristics, this may lead to irrational collective actions for the group. A wide range of organisms from animals to humans often evolve the social attribute of cooperation to meet this challenge. Therefore, cooperation among individuals is of great significance for allowing social organisms to adapt to changes in the natural environment. Based on multi-agent reinforcement learning, we propose a new learning strategy for achieving coordination by incorporating a learning rate that can balance exploration and exploitation. We demonstrate that agents that use the simple strategy improve a relatively collective return in a decision task called the intertemporal social dilemma, where the conflict between the individual and the group is particularly sharp. We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated policies relative to those trained in homogeneous populations.
Ranjana Roy Chowdhury, Shivam Gupta, Sravanthi Chede
In the recent period of time with a lot of social platforms emerging, the relationships among various units can be framed with respect to either positive, negative or no relation. These units can be individuals, countries or others that form the basic structural component of a signed network. These signed networks picture a dynamic characteristic of the graph so formed allowing only few combinations of signs that brings the structural balance theorem in picture. Structural balance theory affirms that signed social networks tend to be organized so as to avoid conflictual situations, corresponding to cycles of unstable relations. The aim of structural balance in networks is to find proper partitions of nodes that guarantee equilibrium in the system allowing only few combination triangles with signed edges to be permitted in graph. Most of the works in this field of networking have either explained the importance of signed graph or have applied the balance theorem and tried to solve problems. Following the recent time trends with each nation emerging to be superior and competing to be the best, the probable doubt of happening of WW-III(World War-III) comes into every individuals mind. Nevertheless, our paper aims at answering some of the interesting questions on World War-III. In this project we have worked with the creation of a signed graph picturing the World War-III participating countries as nodes and have predicted the best possible coalition of countries that will be formed during war. Also, we have visually depicted the number of communities that will be formed in this war and the participating countries in each communities.
Since I wrote the editorial for the June issue, countries around the world have continued to be in the grip of the virus causing COVID-19. Many of us in the northern hemisphere experienced a welcome brief respite in the restrictions imposed by our governments and health authorities over the summer months, only to be followed by a 'second wave' bringing rising infections, hospitalisations and deaths. As I write, the UK passed the symbolic number of 50,000 deaths attributed to COVID, including almost 5,000 in Scotland. Worldwide, the virus is responsible for more than one million deaths.
Social pathology. Social and public welfare. Criminology
The COVID-19 pandemic has affected people's lives around the world on an unprecedented scale. We intend to investigate hoarding behaviors in response to the pandemic using large-scale social media data. First, we collect hoarding-related tweets shortly after the outbreak of the coronavirus. Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020, and dissect the hoarding-related tweets by age, gender, and geographic location. We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population. Furthermore, using topic modeling, we investigate the opinions expressed towards the hoarding behavior by categorizing these topics according to demographic and geographic groups. We also calculate the anxiety scores for the hoarding and anti-hoarding related tweets using a lexical approach. By comparing their anxiety scores with the baseline Twitter anxiety score, we reveal further insights. The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean. Interestingly, beer has the highest calculated anxiety score compared to other hoarded items mentioned in the tweets.
Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT, which uses the concept of label transition. GraphLT assumes that perturbators' random activities deteriorate GCN's performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN's predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN's prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.