Converting Evidence Into Data: The Use Of Law Enforcement Archives As Unobtrusive Measurement

The newly emerging area of Investigative Psychology provides a behavioural science basis for crime detection by examining investigative processes and criminal behaviour. It draws upon a range of material collected by law enforcement agencies that is not widely utilized in the social sciences. This may be regarded as a form of non-reactive, unobtrusive data that has many of the advantages originally promoted by Webb, Campbell, Schwartz and Sechrest (1966) and more recently explored by Lee (2000). The value of such data, derived from police sources, has been demonstrated in a variety of Investigative Psychology studies. However, law enforcement material is not usually collected as data but rather as evidence. Consideration is therefore given to how to address the challenges this poses. The unobtrusive measures derived from police investigations provide a different perspective on crime and other aspects of human actions from that based on more conventional sources of data such as questionnaires and interviews. To assist in the effective use of measures derived from police information a framework for considering this material is proposed reflecting the range of sources of measures that Lee (2000) identified; personal records, running records, physical traces, and simple observation. As in other areas, close attention to the methods of collecting such material can considerably improve its utility. The measures being utilized in Investigative Psychology therefore offer some fruitful directions for other areas of social science research. Development of these measures can also improve the effectiveness of criminal investigations

Offender and offence characteristics of school shooting incidents

School shootings are a concern due to their impact in the local community. This paper aimed to (a) establish frequent characteristics of the offender and offence, (b) explore the differences between offenders who are over the age of 18 years and those who are younger, and (c) consider the underlying themes of the offence characteristics. Data were collected on 28 cases through accessing resources such as West Law and case studies. The majority of the offenders were Caucasian and US citizens and suffered from depression. Their offences were primarily well planned, involved more than three deaths, and resulted in the offender committing suicide. Pearson’s chi-square test and Fisher’s exact test identified significant differences between the two age groups. Offenders who were 18 years of age or under were more likely to experience depression, be US citizens and be linked to the school. Additionally, offenders who were 18 years of age or under were more likely to have stolen their weapons and made threats prior to the incident. Smallest space analysis revealed four thematic regions in relation to the offence characteristics: making an impact, delivering a message, doing unrestrained activity, and targeting specific individuals. These findings have implications for risk assessment and furthering understanding. Keywrods : school shooting; juvenile; offence characteristics; multidimensional scaling; school violenc

Expressive and Instrumental Offending: Reconciling the Paradox of Specialisation and Versatility

Although previous research into specialisation has been dominated by the debate over the existence of specialisation versus versatility, it is suggested that research needs to move beyond the restrictions of this dispute. The current study explores the criminal careers of 200 offenders based on their criminal records, obtained from a police database in the North West of England, aiming to understand the patterns and nature of specialisation by determining the presence of differentiation within their general offending behaviours and examining whether the framework of Expressive and Instrumental offending styles can account for any specialised tendencies that emerge. Fifty-eight offences were subjected to Smallest Space Analysis. Results revealed that a model of criminal differentiation could be identified and that any specialisation is represented in terms of Expressive and Instrumental offending styles

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Autonomy in the AAL : between law and ethics

The growth of Silver Economy calls for a paradigm shift in senior care, where active and healthy ageing is a primary goal. It also coincides with the rapid development of new technologies – AAL being one of them. The AAL combines the advances in the emerging technologies with the need to promote healthy and active ageing experience. This master thesis focuses on the value of individual autonomy and its importance in the context of senior care. The main argument of this research is that individual autonomy is a crucial element in attaining the goal of active and healthy ageing. However, the impact of AAL technology on individual autonomy is uncertain. On one side, AAL's main goal is to enable independent and autonomous living for as long as possible, while on the other side, the AAL by its very design limits individual autonomy. Individual autonomy in the AAL is enabled through legal and ethical norms. The nature of the AAL technology and the contexts and norms under which it operates are dynamic and constantly changing. Therefore, legal regulation needs to be augmented by ethical norms that are fit to meet the ever-changing character of this emerging technology. In particular, ethical technology design principles have a great potential to address the novelty of the AAL and the challenges that European data protection legislation is failing to tackle