Анализ и прогноз преступности в регионах на основе поисковых запросов в Интернет

В статье рассматривается применение поисковых запросов в Интернет для анализа деятельности региональной полиции и прогноза преступлений в регионах. Решение первой задачи базируется на сравнении относительного количества преступлений и соответствующих запросов в различных регионах. При решении второй задачи используется наличие корреляция между динамикой учитываемых преступлений и связанных с ними запросов для построения моделей с прменением МГУА.У статті розглядається застосування пошукових запитів в Інтернет для аналізу діяльності регіональної поліції та прогнозування злочинів у регіонах. Розв’язок першої задачі базується на порівнянні відносної кількості злочинів і відповідних запитів у різних регіонах. При розв’язанні другої задачі використовується наявність кореляції між динамікою враховуваних злочинів і пов’язаних з ними запитів для побудови моделей з використанням МГУА. Ключові слова: пошукові запити, регіональна поліція, прогноз злочинів, МГУА.The paper considers the application of web search queries for analysis of the regional police activity and forecast of crimes in regions. Solution of the first task is based on comparison of the relative number of crimes and corresponding queries in the different regions. When solving the second task, the presence of correlation between the dynamics of crimes and the queries related to these crimes is used to build models using GMDH. Keywords: search queries, regional police, forecast of crimes, GMDH.Эта работа есть расширенная версия статьи Boldyreva A., Alexandrov M., Koshulko O., Sobolevskiy O.: Queries to Internet as a tool for analysis of the regional police work and forecast of the crimes in regions, In: Proc. of 14th Mexican Int. Conf. on Artif. Intell. (MICAI-2016), Springer, LNAI, vol. 10061-10062, 2016, 12 pp

Harnessing the Potential of Online Searches for Understanding the Impact of COVID-19 on Intimate Partner Violence in Italy

Despite the volume of studies leveraging big data to explore socio-demographic phenomena, we still know little about the intersection of digital information and the social problem of intimate partner violence (IPV). This is an important knowledge gap, as IPV remains a pressing public-health concern worldwide, with 35% of women having experienced it over their lifetime and cases rising dramatically in the wake of global crises such as the current COVID-19 pandemic. This study addresses the question of whether online data from Google Trends might help to reach “hard-to-reach” populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV — both potential threat and actual violent cases — in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results combined suggest that online Google searches using selected keywords measuring different aspects of IPV are a powerful tool to track potential threats of IPV before and after global-level crises such as the current COVID-19 pandemic — with stronger predictive power post-crisis — while online searches help to predict actual violence only in post-crises scenarios

Harnessing the Potential of Google Searches for Understanding Dynamics of Intimate Partner Violence Before and After COVID-19 Outbreak

Most social phenomena are inherently complex and hard to measure, often due to under-reporting, stigma, social desirability bias, and rapidly changing external circumstances. This is for instance the case of Intimate Partner Violence (IPV), a highly-prevalent social phenomenon which has drastically risen in the wake of the COVID-19 pandemic. This paper explores whether big data — an increasingly common tool to track, nowcast, and forecast social phenomena in close-to-real time — might help track and understand IPV dynamics. We leverage online data from Google Trends to explore whether online searches might help reach “hard-to-reach” populations such as victims of IPV using Italy as a case-study. We ask the following questions: Can digital traces help predict instances of IPV — both potential threat and actual violent cases — in Italy? Is their predictive power weaker or stronger in the aftermath of crises such as COVID-19? Our results suggest that online searches using selected keywords measuring different facets of IPV are a powerful tool to track potential threats of IPV before and during global-level crises such as the current COVID-19 pandemic, with stronger predictive power post outbreaks. Conversely, online searches help predict actual violence only in post-outbreak scenarios. Our findings, validated by a Facebook survey, also highlight the important role that socioeconomic status (SES) plays in shaping online search behavior, thus shedding new light on the role played by third-level digital divides in determining the forecasting power of digital traces. More specifically, they suggest that forecasting might be more reliable among high-SES population strata

Building Classifiers with GMDH for Health Social Networks (BD AskaPatient)

Health social media offer useful data for patients and doctors concerning both various medicines and treatments. Usually, these data are accompanied by their assessments in 5- star scale. But such a detail classification has small usefulness because patients and doctors, first of all, want to know about negative cases and to study in detail the extreme ones. In the paper we build classifiers of texts just for these cases using combined classes as negative, all others and worst, satisfactory, best. For this, we study possibilities of different GMDH-based algorithms and compare them with the results of other methods. The selection of GMDH is provoked by two circumstances: (a) health social media contain significant informative noise, and (b) GMDH is essentially noise-immunity method. The experimental material is the popular health social network Askapatient

Calm before the storm: the challenges of cloud computing in digital forensics

Cloud computing is a rapidly evolving information technology (IT) phenomenon. Rather than procure, deploy and manage a physical IT infrastructure to host their software applications, organizations are increasingly deploying their infrastructure into remote, virtualized environments, often hosted and managed by third parties. This development has significant implications for digital forensic investigators, equipment vendors, law enforcement, as well as corporate compliance and audit departments (among others). Much of digital forensic practice assumes careful control and management of IT assets (particularly data storage) during the conduct of an investigation. This paper summarises the key aspects of cloud computing and analyses how established digital forensic procedures will be invalidated in this new environment. Several new research challenges addressing this changing context are also identified and discussed

Racial Disparities in Police Crime Victimization

Policing has become a topic of intense public scrutiny and protest in the aftermath of several recent highly questionable and violent police–citizen encounters including the acts of police violence against George Floyd in Minneapolis (MN), Breonna Taylor in Louisville (KY), and Jacob Blake in Kenosha (WI). These encounters have led to large-scale street protests, the legitimization of the Black Lives Matter movement, and what many commentators perceive as a “national reckoning” on the issue of racial justice. The focus of our research is on police crime—a particular form of police misconduct that involves the criminal arrest of police officers. Our work is designed to identify cases in which law enforcement officers have been arrested for any type of criminal offense(s). One area of police scholarship that has thus far been neglected is the relationship between citizen race and the perpetration of police crime. We are aware of no existing empirical studies on whether, and if so, to what degree, citizen race is associated with crimes committed by police officers. The public has been forced to re-examine and question the role and legitimacy of police against the backdrop of protests and concerns about how police may contribute to racial injustice and discrimination. The broadest research issue involved an examination of the association between police crime and the race of the victim. Our goal was to identify and examine any racial disparities of police crime overall and within specific types of police crime. The analyses compared police crimes committed against Black victims to all other police crimes identified within the dataset. More specifically, we examined the degree to which police crimes perpetrated against Black victims tend to be more violent than those perpetrated against non-Black victims. CHAID regression models were utilized to explore any multivariate relationships between race and police crime. Data were derived from published news articles using the Google News search engine and its Google Alerts email update service. Our database currently includes information on more than 18,700 cases of police crime from years 2005–2021. The study utilized data derived from this larger project. The study examined those cases of police crime in which we have identified a victim and recorded information on the race of the victim. The dataset for this study includes information on 865 criminal arrest cases of sworn nonfederal law enforcement officers within the United States from 2005 through 2014

Measuring objective and subjective well-being: dimensions and data sources

AbstractWell-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development

Development and validation of scientific indicators of the relationship between criminality, social cohesion and economic performance

The study intends to contribute to a better understanding of the interactions between criminality, economic performance and social cohesion. We try to achieve this aim by evaluating the existing economic and criminological research and by carrying out own empirical investigation on the basis of international panel data sets from different levels of regional aggregation. Our empirical results with respect to the causes of crime clearly reveal the crime reducing potential of family cohesion and the link between crime and the labour market. Furthermore, we find that higher wealth is associated with higher rates of property crime and of drug-related offences. Drug offences themselves turn out to be robust factors of property crimes. Compared to studies assessing the causes of crime, investigations on its consequences are relatively rare. In our analysis, we investigate the impact of crime on economic performance. We find evidence that employment as well as GDP growth rates are negatively affected by the regional incidence of criminality. Crime ; socio-economic factors ; demographics ; European panel data --

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

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