Catherine Paquette, Taylor Krajewski, Zaire Cullins
et al.
Abstract Background Many people with histories of criminalized drug use resume using drugs after leaving incarceration, yet limited research explores the specific needs and vulnerabilities of these individuals compared to those who remain abstinent. We examined the relationship between post-incarceration drug use and competing psychosocial needs, as well as these needs’ association with views regarding substance use disorder (SUD) treatment. Aims (1) Compare psychosocial needs between individuals with recent drug use post-incarceration and those who were abstinent. (2) Examine which needs are associated with participants’ views on the importance of SUD treatment. Methods Among 244 participants with a history of drug use who were previously incarcerated, currently on community supervision, and had elevated risk of HIV, we compared those who used drugs within 30 days post-release (n = 97) to those who did not (n = 147) on health insurance coverage, alcohol use, food security, employment status, housing stability, social support, and depressive symptoms. We analyzed bivariate associations between these needs and the importance of SUD treatment using Wilcoxon-Mann-Whitney and Jonckheere-Terpstra tests. Results Participants with recent drug use reported higher rates of hazardous alcohol consumption (35.4% vs. 18.1%), food insecurity (58.8% vs. 42.9%), un- or under-employment (61.7% vs. 49.0%), low social support (44.3% vs. 24.7%), and depressive symptoms (50.5% vs. 21.8%). Substance-related problems were significantly linked to higher treatment importance for both groups. Multiple additional competing needs correlated with treatment importance among abstinent individuals. Conclusions Individuals who return to drug use post-incarceration face greater psychosocial challenges. Results highlight the need for disentanglement of access to services from treatment and the provision of comprehensive services regardless of stage of recovery.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
We propose a model of cross-pollination among online social media (OSM) websites, where the dynamics of user interactions mimic insect-mediated pollen transfer by pollinators. A pollinator acts as a vehicle enabling users to visit multiple social media sites- akin to visiting different plants in the same field- within a single browsing session. This approach frames geitonogamy in self-incompatible plant species as analogous to the distribution of web traffic across the social media landscape. A theoretical pollinator, allowing users to choose among social media sites multiple times per trip, drives uneven increases in web traffic across platforms, disproportionately benefiting the largest social networks while providing tangible competitive advantages to smaller OSMs. This heterogeneous landscape fosters monopolistic competition among niche platforms, incentivizing smaller sites to promote cross-pollination despite the larger relative gains to their bigger competitors. Our findings underscore the broader value of cross-platform user engagement, highlighting how cross-pollination dynamics can intensify network effects and bolster interconnectivity. Cross pollination via new pass-through apps facilitates the movement of attention, deepening and distributing engagement across multiple destinations. As pass-through apps gain traction, their disproportionate impact on traffic to social media platforms will incentivize social media platforms, large and small, to embrace cross-pollination dynamics.
Lotfi El Hafi, Kazuma Onishi, Shoichi Hasegawa
et al.
Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. While semi-autonomous CAs intermittently require human teleoperation and supervision, the deployment of fully autonomous CAs remains a challenge. This study evaluates public perception and potential social impacts of fully autonomous CAs for physical support in daily life. To this end, we conducted a large-scale demonstration and survey during Avatar Land, a 19-day public event in Osaka, Japan, where fully autonomous robotic CAs, alongside semi-autonomous CAs, performed daily object retrieval tasks. Specifically, we analyzed responses from 2,285 visitors who engaged with various CAs, including a subset of 333 participants who interacted with fully autonomous CAs and shared their perceptions and concerns through a survey questionnaire. The survey results indicate interest in CAs for physical support in daily life and at work. However, concerns were raised regarding task execution reliability. In contrast, cost and human-like interaction were not dominant concerns. Project page: https://lotfielhafi.github.io/FACA-Survey/.
How can individual agents coordinate their actions to achieve a shared objective in distributed systems? This challenge spans economic, technical, and sociological domains, each confronting scalability, heterogeneity, and conflicts between individual and collective goals. In economic markets, a common currency facilitates coordination, raising the question of whether such mechanisms can be applied in other contexts. This paper explores this idea within social media platforms, where social support (likes, shares, comments) acts as a currency that shapes content production and sharing. We investigate two key questions: (1) Can social support serve as an effective coordination tool, and (2) What role do influencers play in content creation and dissemination? Our formal analysis shows that social support can coordinate user actions similarly to money in economic markets. Influencers serve dual roles, aggregating content and acting as information proxies, guiding content producers in large markets. While imperfections in information lead to a "price of influence" and suboptimal outcomes, this price diminishes as markets grow, improving social welfare. These insights provide a framework for understanding coordination in distributed environments, with applications in both sociological systems and multi-agent AI systems.
On social media sharing platforms, some posts are inherently destined for popularity. Therefore, understanding the reasons behind this phenomenon and predicting popularity before post publication holds significant practical value. The previous work predominantly focuses on enhancing post content extraction for better prediction results. However, certain factors introduced by social platforms also impact post popularity, which has not been extensively studied. For instance, users are more likely to engage with posts from individuals they follow, potentially influencing the popularity of these posts. We term these factors, unrelated to the explicit attractiveness of content, as implicit social factors. Through the analysis of users' post browsing behavior (also validated in public datasets), we propose three implicit social factors related to popularity, including content relevance, user influence similarity, and user identity. To model the proposed social factors, we introduce three supervised contrastive learning tasks. For different task objectives and data types, we assign them to different encoders and control their gradient flows to achieve joint optimization. We also design corresponding sampling and augmentation algorithms to improve the effectiveness of contrastive learning. Extensive experiments on the Social Media Popularity Dataset validate the superiority of our proposed method and also confirm the important role of implicit social factors in popularity prediction. We open source the code at https://github.com/Daisy-zzz/PPCL.git.
Formally announced to the public following former President Donald Trump's bans and suspensions from mainstream social networks in early 2022 after his role in the January 6 Capitol Riots, Truth Social was launched as an "alternative" social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content moderation policies of the existing, mainstream social networks. The subsequent rise of Truth Social has been driven largely by hard-line supporters of the former president as well as those affected by the content moderation of other social networks. These distinct qualities combined with its status as the main mouthpiece of the former president positions Truth Social as a particularly influential social media platform and give rise to several research questions. However, outside of a handful of news reports, little is known about the new social media platform partially due to a lack of well-curated data. In the current work, we describe a dataset of over 823,000 posts to Truth Social and and social network with over 454,000 distinct users. In addition to the dataset itself, we also present some basic analysis of its content, certain temporal features, and its network.
Shahar Dobzinski, Wenzheng Li, Aviad Rubinstein
et al.
We present a constant-factor approximation algorithm for the Nash social welfare maximization problem with subadditive valuations accessible via demand queries. More generally, we propose a template for NSW optimization by solving a configuration-type LP and using a rounding procedure for (utilitarian) social welfare as a blackbox, which could be applicable to other variants of the problem.
Pamela K. Lattimore, Nicholas J. Richardson, Pamela L. Ferguson
et al.
Abstract Background The purpose of the study was to assess the prevalence of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) and to determine whether TBI or PTSD is associated with an increase in general or violent criminal recidivism among a representative sample of released prisoners. In-person interviews were conducted with a stratified random sample of individuals incarcerated with the South Carolina Department of Corrections approximately 90 days prior to the prisoners’ releases. In addition to a variety of items and scales, respondents were screened for TBI and were asked whether they had received a current diagnosis of PTSD. Data were merged with arrest data that provided measures of past criminal involvement and indicators of post-release recidivism (arrest). Arrests were coded as “general” for any arrest charge and “violent” for any violent offense charge. Results Survival analyses indicate that neither TBI nor PTSD predicts time to general recidivism. PTSD (p < 0.01) and age at first arrest (p < 0.01) are significant predictors for violent recidivism and TBI is non-significant at p = 0.09. Results from the negative binomial models indicate that TBI (p < 0.05) and PTSD (p < 0.05) are significantly associated with more post-release violent arrests, but not general arrests. Conclusions TBI and PTSD were found to predict violent offending but not general criminal behavior. These findings demonstrate the need for prison officials to identify individuals with a history of TBI and PTSD and to develop appropriate interventions that could be provided during incarceration to reduce the post-release likelihood of violence.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
Motivated by the impact of emerging technologies on toll parks, this paper studies a problem of equilibrium, social welfare, and revenue for an infinite-server queue. More specifically, we assume that a customer's utility consists of a positive reward for receiving service minus a cost caused by the other customers in the system. In the observable setting, we show the existence, uniqueness, and expressions of the individual threshold, the socially optimal threshold, and the optimal revenue threshold, respectively. Then, we prove that the optimal revenue threshold is smaller than the socially optimal threshold, which is smaller than the individual one. Furthermore, we also extend the cost functions to any finite polynomial function with non-negative coefficients. In the unobservable setting, we derive the joining probabilities of individual and optimal revenue. Finally, using numerical experiments, we complement our results and compare the social welfare and the revenue under these two information levels.
Kishor Jothimurugan, Suguman Bansal, Osbert Bastani
et al.
Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward functions, the user provides high-level specifications that encode the objective of each agent. Then, guided by the structure of the specifications, our algorithm searches over policies to identify one that provably forms an $ε$-Nash equilibrium (with high probability). Importantly, it prioritizes policies in a way that maximizes social welfare across all agents. Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
The core claim of this article is that critical criminology offers us an especially potent framework for interpreting state-corporate crime with the health care industry in the United States as one illustrative case, particularly in the context of the COVID-19 crisis. The unprecedented, surreal pandemic crisis that surfaced in 2020 brought into especially sharp relief many of the core claims of critical criminology in relation to domination, inequality and injustice within a contemporary capitalist political economy, while it also raised the need to broaden critical criminology studies to incorporate the specificities of the health care systems and the pharmaceutical industry. Following this challenge, the article proposes to foster a “critical health criminology” within state-corporate crime research. To do so, this article explores the “big picture” in relation to the COVID-19 pandemic crisis and reveals how it can be understood as a criminological phenomenon. Such a project incorporates the identification of some conceptual issues requiring attention in relation to advancing an enriched form of criminological analysis in these times, and toward building a foundation for a more fully realized twenty-first century criminology.
Social pathology. Social and public welfare. Criminology, Political institutions and public administration (General)
Prashanth Vijayaraghavan, Hugo Larochelle, Deb Roy
With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works have proposed automated techniques to detect hate speech online, these techniques primarily fail to look beyond the textual content. Moreover, few attempts have been made to focus on the aspects of interpretability of such models given the social and legal implications of incorrect predictions. In this work, we propose a deep neural multi-modal model that can: (a) detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and (b) provide interpretable insights into decisions of our model. By performing a thorough evaluation of different modeling techniques, we demonstrate that our model is able to outperform the existing state-of-the-art hate speech classification approaches. Finally, we show the importance of social and cultural context features towards unearthing clusters associated with different categories of hate.
Hannaneh Akrami, Bhaskar Ray Chaudhury, Kurt Mehlhorn
et al.
This paper is merged with arXiv:2107.08965v2. We refer the reader to the full and updated version. We study the problem of allocating a set of indivisible goods among agents with 2-value additive valuations. Our goal is to find an allocation with maximum Nash social welfare, i.e., the geometric mean of the valuations of the agents. We give a polynomial-time algorithm to find a Nash social welfare maximizing allocation when the valuation functions are integrally 2-valued, i.e., each agent has a value either $1$ or $p$ for each good, for some positive integer $p$. We then extend our algorithm to find a better approximation factor for general 2-value instances.
Penyalahgunaan Napza menjadi salah satu kasus yang ada di Indonesia dan membutuhkan perhatian khusus dari berbagai pihak. Pemerintah dan komponen masyarakat perlu melakukan upaya dalam hal pencegahan dan rekayasa lain dalam pemberdayaan terhadap korban penyalahgunaan napza. Peningatan kasus dari setiap tahunnya tidak dapat dielakan. Penelitian ini lebih menitik beratkan pada penelitian pustaka (library research). Kajian dalam penelitian pustaka ini lebih memusatkan kepada pemberdayaan terhadap korban penyalahgunaan napza dalam perspektif manajemen kesejahteraan sosial. Kajian ini memotret aktivitas pemberdayaan melalui kombinasi data literatur dengan data wawancara dan observasi yang ada. Hasil analisis data primer dan sekunder pada penelitian ini adalah (1) penyalahgunaan Napza dan dampak yang ditimbulkan diantaranya (i) dampak individu: dampak mental, dampak fisik, dampak emosional, dampak spiritual, retardasi. (ii) dampak masyarakat/ keluarga: ekonomi, psiksis, dan sosial. (2) pemberdayaan terhadap penyalahgunaan napza dilakukan dengan bentuk sinergi kerjasama antar komponen pemerintah dan masyarakat, dilakukan secara terus menerus dan menyatakan sebagai “perang†terhadap napza. Pemanfaatan modal sosial sebagai pendorong partisipasi aktif dalam penanggulangan penyalahgunaan napza. (3) Manajemen kesejahteraan sosial sebagai upaya pemberdayaan terhadap korban penyalahgunaan napza, memandang upaya pemberdayaan terhadap korban penyalahgunaan napza sebagai tujuan yang memerlukan proses pengorganisasian individu dan masyarakat untuk mencapai keberhasilan pemberdayaan, diawali dengan (i) perencanaan, (ii) pengorganisasian, (iii) pemberian dorongan, dan (iv) pengawasan.
Social pathology. Social and public welfare. Criminology
Ikenna K Ndu, Chidiebere D I Osuorah, Ezinne I Nwaneli
et al.
Introduction: In this study, we sought to determine the severity of caregiving burden among caregivers of children presenting to the emergency room and analyze its associated predictors. Methods: This was a cross-sectional, study carried out on 332 caregivers of children admitted into the children emergency room (CHER) of two tertiary hospitals in Southeast Nigeria. A validated structured questionnaire was administered by an interviewer with the use of an interpreter where necessary. Results: A total of three hundred and thirty-two child–caregiver dyads were enrolled for this study. Fathers were 25.6%, mother 65.4%, and nonparent made up 9.0% of primary caregivers of child in index admission. The mean age of the enrolled children was 2.5 ± 1.9 years with age ranges of 1 month to 16 years. Male-to-female ratio was approximately 0.8. Two hundred and fifty-four (80.6%) of surveyed caregivers experienced high psychosocial burden. On the average, caregivers were faced with moderate burden in the CHER during care of their sick child with a mean caregiver burden score of 1.64 ± 0.67. Caregivers looking after independent children (odds ratio [OR]: 0.1, 95% confidence interval [CI]: 0.2–0.9; P = 0.05), partially dependent children (OR: 0.2, 95% CI: 0.3–0.9; P = 0.040), and those with someone assisting them in the care of admitted and/or children at home (OR: 0.5, 95% CI: 0.2–1.0; P = 0.050) were less likely to experience high psychosocial burden of care as compared with caregivers looking after dependent children and those with no assistance. Conclusion: There is a need to incorporate comprehensive psychosocial and instinctive support for caregivers during the care of their sick children in the emergency room.
Public aspects of medicine, Social pathology. Social and public welfare. Criminology
For most people, playing video games is a normal recreational activity, with little disruption to gamers’ emotional, social, or physical health and well-being. However, for a small percentage of gamers, video gaming can become pathological (Fam, 2018). Substantial research has examined pathological gaming in teens and young adults (Cheng, Cheung, & Wang, 2018; Choo, Gentile, Sim, Khoo, & Liau, 2010), yet pathological gaming in adults (c.f. Holgren, 2017), especially in the context of parenthood, has been relatively ignored. The current study sought to address this limitation by studying associations between pathological gaming characteristics and parenting outcomes in a sample of men and women who have had a child in the last year. Fathers spent more time than mothers playing video games and displayed more pathological video gaming tendencies. Pathological gaming for mothers and fathers was related to increased depressive symptoms. Depressive symptoms mediated the relationship between pathological gaming and decreased feelings of parental efficacy, perceived parental competence, increased parenting stress, and increased perceived impact of parenting. Pathological video game playing was also directly related to decreased feelings of parental efficacy for mothers and fathers. Implications of the results and directions for future research are discussed.
Psychology, Social pathology. Social and public welfare. Criminology
Milad Mirbabaie, Jennifer Fromm, Simone Löppenberg
et al.
A growing number of people use social media to seek information or coordinate relief activities in times of crisis. Thus, social media is increasingly deployed by emergency agencies as well to reach more people in crisis situations. However, the large amount of available data on social media could also be used by emergency agencies to understand how they are perceived by the public and to improve their communication. In this study, we examined the Twitter communication about the German emergency agency "Johanniter-Unfall-Hilfe" by conducting a frequency, sentiment, social network and content analysis. The results reveal that a right-wing political cluster politically instrumentalised an incident related to this agency. Furthermore, some individuals used social media to express criticism. It can be concluded that the use of social media analytics in the daily routine of emergency management professionals can be beneficial for improving their social media communication strategy.
A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.