A framework for the forensic analysis of user interaction with social media

The increasing use of social media, applications or platforms that allow users to interact online, ensures that this environment will provide a useful source of evidence for the forensics examiner. Current tools for the examination of digital evidence find this data problematic as they are not designed for the collection and analysis of online data. Therefore, this paper presents a framework for the forensic analysis of user interaction with social media. In particular, it presents an inter-disciplinary approach for the quantitative analysis of user engagement to identify relational and temporal dimensions of evidence relevant to an investigation. This framework enables the analysis of large data sets from which a (much smaller) group of individuals of interest can be identified. In this way, it may be used to support the identification of individuals who might be ‘instigators’ of a criminal event orchestrated via social media, or a means of potentially identifying those who might be involved in the ‘peaks’ of activity. In order to demonstrate the applicability of the framework, this paper applies it to a case study of actors posting to a social media Web site.

Latent learning in end stage renal disease (ESRD)

Cognitive functions such as attention and memory are known to be impaired in End Stage Renal Disease (ESRD), but the sites of the neural changes underlying these impairments are uncertain. Patients and controls took part in a latent learning task, which had previously shown a dissociation between patients with Parkinson’s disease and those with medial temporal damage. ESRD patients (n=24) and age and education-matched controls (n=24) were randomly assigned to either an exposed or unexposed condition. In Phase 1 of the task, participants learned that a cue (word) on the back of a schematic head predicted that the subsequently seen face would be smiling. For the exposed (but not unexposed) condition, an additional (irrelevant) colour cue was shown during presentation. In Phase 2, a different association, between colour and facial expression, was learned. Instructions were the same for each phase: participants had to predict whether the subsequently viewed face was going to be happy or sad. No difference in error rate between the groups was found in Phase 1, suggesting that patients and controls learned at a similar rate. However, in Phase 2, a significant interaction was found between group and condition, with exposed controls performing significantly worse than unexposed (therefore demonstrating learned irrelevance). In contrast, exposed patients made a similar number of errors to unexposed in Phase 2. The pattern of results in ESRD was different from that previously found in Parkinson’s disease, suggesting a different neural origin.

The nature of impairments of memory in patients with end-stage renal disease (ESRD)

Possible impairments of memory in end-stage renal disease (ESRD) were investigated in two experiments. In Experiment 1, in which stimulus words were presented visually, participants were tested on conceptual or perceptual memory tasks, with retrieval being either explicit or implicit. Compared with healthy controls, ESRD patients were impaired when memory required conceptual but not when it required perceptual processing, regardless of whether retrieval was explicit or implicit. An impairment of conceptual implicit memory (priming) in the ESRD group represented a previously unreported deficit compared to healthy aging. There were no significant differences between pre- and immediate post-dialysis memory performance in ESRD patients on any of the tasks. In Experiment 2, in which presentation was auditory, patients again performed worse than controls on an explicit conceptual memory task. We conclude that the type of processing required by the task (conceptual vs. perceptual) is more important than the type of retrieval (explicit vs. implicit) in memory failures in ESRD patients, perhaps because temporal brain regions are more susceptible to the effects of the illness than are posterior regions.

Multi-attribute collaborative filtering recommendation based on improved group decision-making

Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.