Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

Scientometrics - Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize...     

Dynamic increasing returns and innovation diffusion: bringing Polya Urn processes to the empirical data

The patterns of innovation diffusion are well approximated by the logistic curves. This is the robust empirical fact confirmed by many studies in innovations dynamics. Here, we show that the logistic pattern of innovation diffusion can be replicated by the time-dependent stochastic process with positive feedbacks along the diffusion trajectory. The dynamic increasing returns process is modelled by Polya Urns. So far, Urn models have been mostly used to study the [path-dependent] limit properties. On the contrary, this work focuses on the transient [finite time] properties studying the conditions under which urn models capture the logistic trajectories which often track empirical diffusion process. As examples, we calibrate the process to match several cases of diffusion of motor ships in European countries.     

Using data to improve medical practice by measuring processes and outcomes of care

BACKGROUND: The purpose of this article is to help clinicians expand their use of data to improve medical practice performance and to do improvement research. Clinical practices can be viewed as small, complex organizations (microsystems) that produce services for specific patient populations. These services can be greatly improved by embedding measurement into the flow of daily work in the practice. WHY DO IT?: Four good reasons to build measures into daily medical practice are to (1) diagnose strengths and weaknesses in practice performance; (2) improve and innovate in providing care and services using improvement research; (3) manage patients and the practice; and (4) evaluate changes in results over time. It is helpful to have a "physiological" model of a medical practice to analyze the practice, to manage it, and to improve it. One model views clinical practices as microsystems that are designed to generate desired health outcomes for specific subsets of patients and to use resources efficiently. This article provides case study examples to show what an office-based practice might look like if it were using front-line measurement to improve care and services most of the time and to conduct clinical improvement research some of the time. WHAT ARE THE PRINCIPLES FOR USING DATA TO IMPROVE PROCESSES AND OUTCOMES OF CARE?: Principles reflected in the case study examples--such as "Keep Measurement Simple. Think Big and Start Small" and "More Data Is Not Necessarily Better Data. Seek Usefulness, Not Perfection, in Your Measures"--may help guide the development of data to study and improve practice. HOW CAN A PRACTICE START TO USE DATA TO IMPROVE CARE AND CONDUCT IMPROVEMENT RESEARCH?: Practical challenges are involved in starting to use data for enhancing care and improvement research. To increase the odds for success, it would be wise to use a change management strategy to launch the startup plan. Other recommendations include "Establish a Sense of Urgency. (Survival Is Not Mandatory)" and "Create the Guiding Coalition. (A Small, Devoted Group of People Can Change the World)." SUMMARY: Over the long term, we must transform thousands of local practice cultures so that useful data are used every day in countless ways to assist clinicians, support staff, patients, families, and communities.     

Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level

There has been considerable research conducted over the last 40 years using traffic safety-related events to support road safety analyses. Dating back to traffic conflict studies from the 1960s these observational studies of driver behavior have been criticized due to: poor quality data; lack of available and useful exposure measures linked to the observations; the incomparability of self-reported safety-related events; and, the difficulty in assessing culpability for safety-related events. This study seeks to explore the relationships between driver characteristics and traffic safety-related events, and between traffic safety-related events and crash involvement while mitigating some of those limitations. The Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study dataset, in which the participants’ vehicles were instrumented with various cameras and sensors during the study period, was used for this study. The study data set includes 90 drivers observed for 12–13 months driving. This study focuses on single vehicle run-off-road safety-related events only, including 14 crashes and 182 safety-related events (30 near crashes, and 152 crash-relevant incidents). Among the findings are: (1) drivers under age 25 are significantly more likely to be involved in safety-related events and crashes; and (2) significantly positive correlations exist between crashes, near crashes, and crash-relevant incidents. Although there is still much to learn about the factors affecting the positive correlation between safety-related events and crashes, a Bayesian multivariate Poisson log-normal model is shown to be useful to quantify the associations between safety-related events and crash risk while controlling for driver characteristics.     

Government policies and attitudes to social media use among users in Turkey: The role of awareness of policies,political involvement,online trust,and party identification

Few research studies have examined the impact of government policies toward social media on individuals’ attitudes to social media use, particularly when these policies aim to denounce and control social media platforms, as was the case in Turkey in 2013–2016. A conceptual model, based on the Theory of Planned Behavior (Ajzen, 2005) [1], was proposed to investigate the mediating role of awareness of government policies, degree of political involvement, online trust, and the moderating role of party identification in predicting the attitudes to social media use. Data were collected through a survey of 653 social media users in Istanbul, Turkey (mean age = 31.76, SD = 10.96; 40 % women, 83 % Turkish ethnicity) in September 2015. Using PLS-SEM modelling, the awareness of government policies, the degree of political involvement, and the online trust were found to partially mediate the relationship between the frequency of social media use and the attitudes to social media use for the users of Twitter, YouTube, and Instagram, while the moderating role of party identification was not significant in this model. The results provide additional support for the role of social context and past behaviors in predicting the attitudes and future intentions in the use of digital communication technologies.