Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

Towards Detecting Rumours in Social Media

The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords

Real-time classification of malicious URLs on Twitter using Machine Activity Data

Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person’s machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. ‘real-time’). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event – the Superbowl – and test it using data from another large sporting event – the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance

The elements of a computational infrastructure for social simulation

Applications of simulation modelling in social science domains are varied and increasingly widespread. The effective deployment of simulation models depends on access to diverse datasets, the use of analysis capabilities, the ability to visualize model outcomes and to capture, share and re-use simulations as evidence in research and policy-making. We describe three applications of e-social science that promote social simulation modelling, data management and visualization. An example is outlined in which the three components are brought together in a transport planning context. We discuss opportunities and benefits for the combination of these and other components into an e-infrastructure for social simulation and review recent progress towards the establishment of such an infrastructure

Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted

A quasi-experimental study to mobilize rural low-income communities to assess and improve the ecological environment to prevent childhood obesity

Citation: Peters, P., Gold, A., Abbott, A., Contreras, D., Keim, A., Oscarson, R., . . . Mobley, A. R. (2016). A quasi-experimental study to mobilize rural low-income communities to assess and improve the ecological environment to prevent childhood obesity. Bmc Public Health, 16, 7. doi:10.1186/s12889-016-3047-4Background: The Ecological Model of Childhood Overweight focuses on characteristics that could affect a child's weight status in relation to the multiple environments surrounding that child. A community coaching approach allows community groups to identify their own strengths, priorities and identity. Little to no research currently exists related to community-based efforts inclusive of community coaching in creating environmental change to prevent childhood obesity particularly in rural communities. Methods: A quasi-experimental study will be conducted with low-income, rural communities (n = 14) in the North Central region of the United States to mobilize capacity in communities to create and sustain an environment of healthy eating and physical activity to prevent childhood obesity. Two rural communities within seven Midwestern states (IN, KS, MI, OH, ND, SD, WI) will be randomly assigned to serve as an intervention or comparison community. Coalitions will complete assessments of their communities, choose from evidence-based approaches, and implement nutrition and physical activity interventions each year to prevent childhood obesity with emphasis on policy, system or environmental changes over four years. Only intervention coalitions will receive community coaching from a trained coach. Outcomes will be assessed at baseline, annually and project end using previously validated instruments and include coalition self-assessments, parental perceptions regarding the built environment, community, neighborhood, and early childhood environments, self-reflections from coaches and project staff, ripple effect mapping with coalitions and, final interviews of key stakeholders and coaches. A mixed-methods analysis approach will be used to evaluate if Community Coaching enhances community capacity to create and sustain an environment to support healthy eating and physical activity for young children. ANOVA or corresponding non-parametric tests will be used to analyze quantitative data relating to environmental change with significance set at P < .05. Dominant emergent themes from the qualitative data will be weaved together with quantitative data to develop a theoretical model representing how communities were impacted by the project. Discussion: This project will yield data and best practices that could become a model for community development based approaches to preventing childhood obesity in rural communities