Public policy decisions are typically justified using a narrow set of headline indicators, leaving many downstream social impacts unstructured and difficult to compare across policies. We propose PPCR-IM, a system for multi-layer DAG-based consequence reasoning and social indicator mapping that addresses this gap. Given a policy description and its context, PPCR-IM uses an LLM-driven, layer-wise generator to construct a directed acyclic graph of intermediate consequences, allowing child nodes to have multiple parents to capture joint influences. A mapping module then aligns these nodes to a fixed indicator set and assigns one of three qualitative impact directions: increase, decrease, or ambiguous change. For each policy episode, the system outputs a structured record containing the DAG, indicator mappings, and three evaluation measures: an expected-indicator coverage score, a discovery rate for overlooked but relevant indicators, and a relative focus ratio comparing the systems coverage to that of the government. PPCR-IM is available both as an online demo and as a configurable XLSX-to-JSON batch pipeline.
Gianluca Nogara, Erfan Samieyan Sahneh, Matthew R. DeVerna
et al.
Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform's public launch, an influx of new users, particularly those posting in English and Japanese, contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky's moderation efforts have been effective.
Georgios Karystianis, Sachiko Kita, Fiona Lerigo
et al.
Abstract Background setting Domestic violence (DV) perpetrated against male victims has received little attention in the literature, since men are generally the perpetrators rather than victims of DV. This study examines the characteristics of adult male victims and female Persons of Interest (POIs) suspected and/or charged with perpetrating a DV offence in an intimate relationship. Methods We analyzed the results from a text mining study on half a million (492,393) police-attended DV events from 2005 to 2016 in New South Wales (Australia). 7.3% (13,896) events involving an adult male victim and a female POI in an intimate relationship were included. Results Over three-quarters (77.5%; 10,775) of DV events had at least one abuse type recorded, with the most common being “unspecified assault” (57.3%), followed by verbal abuse (34.1%), and punching (29.1%). Half of events (51.2%; 7,128) had an injury recorded by the police, with “cut/abrasion(s)” the most common (41.6%), followed by “red mark/sign” (25.4%), and “bruising” (15.8%). A total of 2,196 (15.8%) DV events had a mention of a mental illness for the POIs and 570 (4.1%) for the victims. Mood disorders had the most mentions for both POIs (37.0%) and victims (32.8%). Among victims, anxiety related disorders saw the largest increase (14.0%) in mentions from 2005 to 2016, followed by depression (8.0%). Conclusion Our findings represent population level data insights from DV events involving an adult male victim in an intimate relationship with a female POI. Our findings align with existing studies suggesting that female POIs are more likely than male POIs to use objects/weapons, employ verbal abuse, and perform minor acts of physical violence. Female POIs had 4 times the number of mental illness mentions than male victims indicating that mental illness could be a risk factor for DV, while the increase in anxiety disorders and depression for male victims corresponds with research that associates mental illnesses and DV victimhood. This study highlights the need for a greater awareness and support for male victims of DV.
Science (General), Social pathology. Social and public welfare. Criminology
Richard Abayomi Aborisade, Akoji Ocheja, Babatunde Adekunle Okuneye
Qualitative research on the experiences of victims of online dating romance scams is limited. Following a phenomenological framework, this study explores victims’ accounts of interactions with offenders from first contact, development of romance, patterns of exploitation, eventual revelation, and financial and emotional costs of dating fraud. Ten participants from six countries who were victims of Nigerian romance fraudsters took part in one-to-one, semi-structured video interviews and an interpretative phenomenological analysis (IPA) was carried out. Four superordinate themes were identified from participants’ experiences: i) online romance, ii) exploitations, iii) revelations, and iv) reactions. These themes highlighted the uniqueness of the experiences of victims of online dating scams and the depth of emotional loss suffered after their victimization. Findings suggested that offenders target middle-aged women with troubled marriages or widows with inherited wealth, engage in lengthy pre-dating friendships with their targets, and deploy different forms of emotional blackmail to exploit their victims. These were found to have severe financial and long-term emotional consequences on victims. Participants reported they sought legal redress as a recovery strategy from their emotional sufferings. These findings have important practical and policy implications if online romance dating scams, and their financial and non-financial consequences are to be addressed.
Social pathology. Social and public welfare. Criminology
William J. Bingley, S. Alexander Haslam, Janet Wiles
A core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence, 'socially minded intelligence', that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.
Statistical parity metrics have been widely studied and endorsed in the AI community as a means of achieving fairness, but they suffer from at least two weaknesses. They disregard the actual welfare consequences of decisions and may therefore fail to achieve the kind of fairness that is desired for disadvantaged groups. In addition, they are often incompatible with each other, and there is no convincing justification for selecting one rather than another. This paper explores whether a broader conception of social justice, based on optimizing a social welfare function (SWF), can be useful for assessing various definitions of parity. We focus on the well-known alpha fairness SWF, which has been defended by axiomatic and bargaining arguments over a period of 70 years. We analyze the optimal solution and show that it can justify demographic parity or equalized odds under certain conditions, but frequently requires a departure from these types of parity. In addition, we find that predictive rate parity is of limited usefulness. These results suggest that optimization theory can shed light on the intensely discussed question of how to achieve group fairness in AI.
The present study is conducted with the teachers in self-financing colleges in Coimbatore numbering 45 samples with the objective of finding out the socio-demographic characteristics of the respondents and their level of Job security and insecurity. The scientific tool is used for data and the collected data are systematically analyzed. The article portrays the methodology adopted for conducting the study. The paper provides the statistical information which resulted after applying tests such as, mean, standard deviation, one-way analysis of variance, t-test, and Karl Pearson’s correlation coefficient.
The major finding of the study is that more than half of the respondents i.e., 52 percent of the respondents have a high level of insecurity feelings. Based on the findings, Suitable recommendations are suggested with the perspectives such as open-door policy, recreational provisions, grievance redressal procedures, to be followed in the self-finance colleges. The current article may help the readers to understand the job security status of teachers in self-financing colleges.
Social pathology. Social and public welfare. Criminology
Historically, serial homicide has been defined in various ways by experts. Recently, there have been renewed efforts to arrive at a consensus definition, yet these efforts have not yet been resolved. At the heart of the controversy appears to be the prioritization of either qualitative definitional features, such as offenders’ intentions and motives, or more observable quantitative features, specifically a minimum threshold of completed murders. The present technical note briefly summarizes this controversy before considering new empirical and theoretical research developments. These developments support a definition that includes a three-victim minimum threshold of forensically linked murderers by the same person(s), occurring in separate events over time, wherein a primary motive is often personal gratification (leisure experience).
Social pathology. Social and public welfare. Criminology, Analytical chemistry
We develop a model of opinion dynamics where agents in a social network seek to learn a ground truth among a set of competing hypotheses. Agents in the network form private beliefs about such hypotheses by aggregating their neighbors' publicly stated beliefs, in an iterative fashion. This process allows us to keep track of scenarios where private and public beliefs align, leading to population-wide consensus on the ground truth, as well as scenarios where the two sets of beliefs fail to converge. The latter scenario - which is reminiscent of the phenomenon of cognitive dissonance - is induced by injecting 'conspirators' in the network, i.e., agents who actively spread disinformation by not communicating accurately their private beliefs. We show that the agents' cognitive dissonance non-trivially reaches its peak when conspirators are a relatively small minority of the population, and that such an effect can be mitigated - although not erased - by the presence of 'debunker' agents in the network.
The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.
Junxian Wang, Wesley P. Chan, Pamela Carreno-Medrano
et al.
Recent protocols and metrics for training and evaluating autonomous robot navigation through crowds are inconsistent due to diversified definitions of "social behavior". This makes it difficult, if not impossible, to effectively compare published navigation algorithms. Furthermore, with the lack of a good evaluation protocol, resulting algorithms may fail to generalize, due to lack of diversity in training. To address these gaps, this paper facilitates a more comprehensive evaluation and objective comparison of crowd navigation algorithms by proposing a consistent set of metrics that accounts for both efficiency and social conformity, and a systematic protocol comprising multiple crowd navigation scenarios of varying complexity for evaluation. We tested four state-of-the-art algorithms under this protocol. Results revealed that some state-of-the-art algorithms have much challenge in generalizing, and using our protocol for training, we were able to improve the algorithm's performance. We demonstrate that the set of proposed metrics provides more insight and effectively differentiates the performance of these algorithms with respect to efficiency and social conformity.
Hydroxychloroquine (HCQ) is used to prevent or treat malaria caused by mosquito bites. Recently, the drug has been suggested to treat COVID-19, but that has not been supported by scientific evidence. The information regarding the drug efficacy has flooded social networks, posting potential threats to the community by perverting their perceptions of the drug efficacy. This paper studies the reactions of social network users on the recommendation of using HCQ for COVID-19 treatment by analyzing the reaction patterns and sentiment of the tweets. We collected 164,016 tweets from February to December 2020 and used a text mining approach to identify social reaction patterns and opinion change over time. Our descriptive analysis identified an irregularity of the users' reaction patterns associated tightly with the social and news feeds on the development of HCQ and COVID-19 treatment. The study linked the tweets and Google search frequencies to reveal the viewpoints of local communities on the use of HCQ for COVID-19 treatment across different states. Further, our tweet sentiment analysis reveals that public opinion changed significantly over time regarding the recommendation of using HCQ for COVID-19 treatment. The data showed that high support in the early dates but it significantly declined in October. Finally, using the manual classification of 4,850 tweets by humans as our benchmark, our sentiment analysis showed that the Google Cloud Natural Language algorithm outperformed the Valence Aware Dictionary and sEntiment Reasoner in classifying tweets, especially in the sarcastic tweet group.
James Johndrow, Kristian Lum, Maria Gargiulo
et al.
Understanding the number of individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward. Here we present estimates of the extent to which confirmed cases in the United States undercount the true number of infections, and analyze how effective social distancing measures have been at mitigating or suppressing the virus. Our analysis uses a Bayesian model of COVID-19 fatalities with a likelihood based on an underlying differential equation model of the epidemic. We provide analysis for four states with significant epidemics: California, Florida, New York, and Washington. Our short-term forecasts suggest that these states may be following somewhat different trajectories for growth of the number of cases and fatalities.
Abstract The above article, published online on 25 May 2011 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted at the request of the authors and by agreement with the journal editors and Wiley Periodicals, Inc. The second author, Eric A. Stewart, in the course of responding to concerns raised with the data and analysis, identified a mistake in the way the original data were merged. This, in conjunction with the discovery of other coding and transcription errors, collectively exceeded what the authors believed to be acceptable for a published paper. They therefore voluntarily requested that the paper be retracted. The third author, Justin Pickett, also requested retraction. He has publicly stated his view that the identified discrepancies are not attributable to researcher error.
Miriam Abramovay, Valéria Cristina de Oliveira, Flávia Pereira Xavier
et al.
Miriam Abramovay é um dos principais nomes quando se trata de pesquisas sobre violência nas escolas, juventude e educação. Ela é socióloga e doutora em Ciências da Educação pela Université Lumiere Lyon 2 - França. Nesta entrevista, a pesquisadora desenhou um quadro sobre o tema da violência nas escolas no Brasil, a trajetória e possibilidades de pesquisas futuras no país. Além disso, Miriam Abramovay apresenta suas impressões sobre bullying, escolas militares e formação de professores.
Social pathology. Social and public welfare. Criminology
El artículo presenta la categoría de hechos ciberfísicos, una propuesta para el análisis y la delimitación de amenazas en el régimen híbrido del ciberespacio. Se propone la hipótesis de que América Latina, y en particular México, no comprenden en sus Estrategias Nacionales de Ciberseguridad (ENCS) la naturaleza de las ciberagresiones ni la posibilidad de que una crisis surgida en el ciberespacio salte al espacio físico o material. Para probarla se presenta el contexto de la ciberseguridad en la región y se realiza una crítica de la ENCS de México. Después se hace un análisis comparativo de cinco casos de interés y referencia entre los estudios de ciberseguridad, para introducir el concepto de hecho ciberfísico. Por último, se aplica la propuesta a un estudio de caso y se muestra su utilidad para las ENCS, así como su grado de impacto en la esfera de la seguridad pública y nacional.
Abstract
The article introduces the category of cyber-physical facts, a proposal for the analysis and delimitation of threats in the hybrid regime of cyberspace. The research objective is to test the hypothesis that Latin America, and Mexico in particular, do not understand in their National Cybersecurity Strategies (NCSS) the nature of cyber-attacks, nor the possibility that a crisis arising in cyberspace will jump into the physical or material ground. To prove this, the context of cybersecurity in the region is presented and also a critique of the NCSS of Mexico. Then, the article makes a comparative analysis of five cases of interest and reference in cybersecurity studies to introduce the concept of cyber-physical fact. Finally, this proposal is applied to a case study, and its usefulness to the NCSS is shown, as well as its degree of impact in the sphere of public and national security.
Social Sciences, Social pathology. Social and public welfare. Criminology
Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response.Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined.This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management. At last,some advanced topics and suggestions of water related social media applications from data collection, data quality management, fake news detection, privacy issues, algorithms and platforms was present to hydroinformatics managers and researchers based on previous discussion.
Social media platforms like Facebook and Twitter permit experiments to be performed at minimal cost on populations of a size that scientists might previously have dreamt about. For instance, one experiment on Facebook involved over 60 million subjects. Such large scale experiments introduce new challenges as even small effects when multiplied by a large population can have a significant impact. Recent revelations about the use of social media to manipulate voting behaviour compound such concerns. It is believed that the psychometric data used by Cambridge Analytica to target US voters was collected by Dr Aleksandr Kogan from Cambridge University using a personality quiz on Facebook. There is a real risk that researchers wanting to collect data and run experiments on social media platforms in the future will face a public backlash that hinders such studies from being conducted. We suggest that stronger safe guards are put in place to help prevent this, and ensure the public retain confidence in scientists using social media for behavioural and other studies.