Indiana University-Purdue University Indianapolis (IUPUI) / Social harm involves incidents resulting in physical, financial, and emotional hardships such as crime, drug overdoses and abuses, traffic accidents, and suicides. These incidents require various law-enforcement and emergency responding agencies to coordinate together for mitigating their impact on the society. With the advent of advanced networking and computing technologies together with data analytics, law-enforcement agencies and people in the community can work together to proactively reduce social harm. With the aim of effectively mitigating social harm events in communities, this thesis introduces a distributed web application, Community Data Analytic for Social Harm (CDASH). CDASH helps in collecting social harm data from heterogenous sources, analyzing the data for predicting social harm risks in the form of geographic hotspots and conveying the risks to law-enforcement agencies. Since various stakeholders including the police, community organizations and citizens can interact with CDASH, a need for a trust framework arises, to avoid fraudulent or mislabeled incidents from misleading CDASH. The enhanced system, called Trusted-CDASH (T-CDASH), superimposes a trust estimation framework on top of CDASH. This thesis discusses the importance and necessity of associating a degree of trust with each social harm incident reported to T-CDASH. It also describes the trust framework with different trust models that can be incorporated for assigning trust while examining their impact on prediction accuracy of future social harm events. The trust models are empirically validated by running simulations on historical social harm data of Indianapolis metro area.

Disertacijoje vertinta su alkoholio vartojimu siejama socialinė ir ekonominė žala Lietuvoje. Pagrindiniai darbo uždaviniai buvo įvertinti su alkoholio vartojimu susijusį mirtingumą, ligotumą ir netektą darbingumą, nustatyti su alkoholio vartojimu susijusių teisėtvarkos pažeidimų apimtis bei įvertinti su alkoholio vartojimu susijusią ekonominę žalą Lietuvoje 2010 m. Tyrime analizuoti Higienos instituto, SVEIDRA, Neįgalumo ir darbingumo nustatymo tarnybos, Statistikos departamento, Informatikos ir ryšių departamento, Valstybinio psichikos sveikatos centro, Valstybinės ligonių kasos, Lietuvos kelių policijos tarnybos, Kalėjimų departamento bei Socialinės apsaugos ir darbo ministerijos duomenys. Analizuojant alkoholio vartojimo žalą sveikatai iš viso buvo vertintos 55 ligos ir būklės (ar jų grupės), visiškai ar iš dalies siejamos su alkoholinių gėrimų vartojimu. 22 iš šių būklių visiškai siejamos tik su alkoholinių gėrimų vartojimu. Likusi dalis – iš dalies alkoholio sąlygojamos būklės, kurių tam tikros proporcijos priskyrimas alkoholio žalai pagrįstas remiantis Lietuvai apskaičiuotomis alkoholiui priskiriamomis dalimis (angl. Alcohol-attributable fractions). Disertacijos rezultatai parodė, kad alkoholio vartojimo sąlygojama socialinė ir ekonominė žala atsispindi daugelyje socialinės gerovės sričių, o tiesiogiai sveikatos priežiūrai tenkanti alkoholio vartojimo sukeliamos socialinės ir ekonominės žalos dalis tėra tik vienas iš daugelio šios žalos komponentų. / The aim of this dissertation was to evaluate the social and economic harm of alcohol in Lithuania. The main objectives were to estimate alcohol-attributable mortality, morbidity, disabilities, to evaluate alcohol related violations of law, and to estimate alcohol-attributable economic costs in Lithuania in 2010. The study includes the data from Institute of Hygiene, SVEIDRA, Disability and Capacity Assessment Service, Department of Statistics, Information Technology and Communications Department, State Mental Health Center, State Patient Fund, Lithuanian Traffic Police Office, Prison Department and Ministry of Social Security and Labour. In total this study included 55 conditions and groups of conditions that are fully or partially attributable to alcohol. 22 of those conditions are 100 per cent attributable to alcohol. The rest were partially attributable to alcohol, which were estimated by applying the Lithuanian specific Alcohol-attributable fractions. The results of this dissertation have identified that alcohol-attributable social and economic harm to society is widely spread across many social welfare sectors, as well as direct health care costs is only one of many alcohol-attributable harm components.

Indiana University-Purdue University Indianapolis (IUPUI) / The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events.

Human rights violations and exploitation of migrant workers in Qatar is something which has been extensively researched during recent years. However, a lot of these studies are focusing on specific rights violations; the functioning of the kafala-system; responsibility of actions; or economic gains of Qatar. To a lesser extent, focus have been pointed towards the structural pillars of the Qatari society and how the government is using these structures as means of controlling, and thereby exploiting, migrant workers. Thus, this thesis is striving to fill this research gap by analyzing how different structural facets have been utilized by the state of Qatar in order to control and manipulate these people. The study is working with a qualitative content analysis and is utilizing a specific branch of social harm theory deemed as work-based harm. As such, different angles of structural control are examined and how this control is implicit in worker exploitation. It is concluded that extensive control mechanisms within both political, legal, cultural and economic pillars have been used by the state of Qatar as means of exploitation.

Indiana University-Purdue University Indianapolis (IUPUI) / Increasing rates of social harm events and plethora of text data demands the need of employing text mining techniques not only to better understand their causes but also to develop optimal prevention strategies. In this work, we study three social harm issues: crime topic models, transitions into drug addiction and homicide investigation chronologies. Topic modeling for the categorization and analysis of crime report text allows for more nuanced categories of crime compared to official UCR categorizations. This study has important implications in hotspot policing. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. We further explore the transitions into drug addiction using Reddit data. We proposed a prediction model to classify the users’ transition from casual drug discussion forum to recovery drug discussion forum and the likelihood of such transitions. Through this study we offer insights into modern drug culture and provide tools with potential applications in combating opioid crises. Lastly, we present a knowledge graph based framework for homicide investigation chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of key features that determine whether a homicide is ultimately solved. For this purpose we perform named entity recognition to determine witnesses, detectives and suspects from chronology, use keyword expansion to identify various evidence types and finally link these entities and evidence to construct a homicide investigation knowledge graph. We compare the performance over several choice of methodologies for these sub-tasks and analyze the association between network statistics of knowledge graph and homicide solvability.

This thesis aims to outline how the labour exploitation of migrant workers is problematized in contemporary Canadian labour migration policy discourse; the consequences of this problematization(s), and possibilities for creating an alternative critical space for discourse. Using concepts of (social) harm, (un)freedom, and (hyper)precarity as departure points for analysis, this project endeavours to untangle problematizations of migrant worker exploitation from their representations in Canadian policy discourse through a post-structural policy analysis. From this theoretical perspective, policy discourse will be analyzed employing Carol Bacchi’s (2009) ‘What is the Problem Represented to Be?’ (WPR) methodology. With this theoretical and methodological basis, the goal of this thesis is to make space to open a critical dialogue between the consequences of the global neoliberal capitalist order, Canadian policy discourse, the Canadian border/migration regime, and the conditions of migrant labourers caught in the middle of this complex nexus.

Increasing rates of social harm events and plethora of text data demands the need of employing text mining techniques not only to better understand their causes but also to develop optimal prevention strategies. In this work, we study three social harm issues: crime topic models, transitions into drug addiction and homicide investigation chronologies. Topic modeling for the categorization and analysis of crime report text allows for more nuanced categories of crime compared to official UCR categorizations. This study has important implications in hotspot policing. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. We further explore the transitions into drug addiction using Reddit data. We proposed a prediction model to classify the users’ transition from casual drug discussion forum to recovery drug discussion forum and the likelihood of such transitions. Through this study we offer insights into modern drug culture and provide tools with potential applications in combating opioid crises. Lastly, we present a knowledge graph based framework for homicide investigation chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of key features that determine whether a homicide is ultimately solved. For this purpose<br>we perform named entity recognition to determine witnesses, detectives and suspects from chronology, use keyword expansion to identify various evidence types and finally link these entities and evidence to construct a homicide investigation knowledge graph. We compare the performance over several choice of methodologies for these sub-tasks and analyze the association between network statistics of knowledge graph and homicide solvability. <br>