Studying the community impact of mass violence using a Big Data approach from social media data (e.g., Twitter) offers traumatic stress researchers an unprecedented opportunity to study and clarify theoretical assumptions using large-scale, observational, ecologically valid data. We describe challenges and benefits of working with Twitter data and briefly review studies that used Twitter data to explore community responses to mass violence. We then demonstrate the use of Twitter data to examine community responses to a specific event: the 2015 San Bernardino terrorist attack, in which 14 people were killed and 22 were wounded. In a 6-week time frame around this attack, we evaluated the time course of community-level negative emotion. We downloaded 1.16 million tweets, representing 25,894 users from San Bernardino, CA, and a matched control community, Stockton, CA. All tweets were coded in R using the Linguistic Inquiry and Word Count (LIWC) negative emotion dictionary. A piecewise regression technique with a discontinuity analysis was used to evaluate pre- and postevent trajectories of negative emotion across the study window. Controlling for within-user variability, negative emotion increased by 6.2%, β = .182, SE = .014, p < .001, in San Bernardino on the day of the attack and remained elevated for 5 days; no elevation was observed in Stockton. We discuss how data-driven text analytic techniques are useful for exploring Twitter content generated after collective traumas and describe challenges and opportunities accompanying analyses of social media data to understand the impact of mass violence on affected populations.
Introduction: The happiness and well-being of individuals are among the most important components of life. However, there remains a lack of evidence regarding the relationships between students' happiness, vigor, and self-esteem on the one hand and various complex factors on the other hand.
Methods: We conducted a cross-sectional study involving 397 students from various Lithuanian universities.
Background: Digital knowledge translation (KT) interventions play a crucial role in advancing adolescent sexual and reproductive health (ASRH). Despite the extensive literature on their effectiveness, there's a lack of synthesized evidence on the efficacy of digital KT tools for adolescent ASRH globally.
Objectives: This review aimed to systematically identify and map existing empirical evidence on digital KT tools targeting ASRH outcomes and identify research gaps.
Introduction: It has been shown that public stigma towards people with schizophrenia hinders their psychosocial integration. Public stigma expresses itself through lexical labels used in the print media and social networks, heightening the internalization of stigma in this population, a phenomenon known as internalised stigma or self-stigma. This paper analyses the diffusion in the mass media of two dimensions of stigma: public stigma and self-stigma.
Introduction And Objectives: The beliefs and opinions of the general population are based substantially on mass media, which often equates mental disorders with violence and criminality. These stigmatising depictions contribute to the development and persistence of negative attitudes towards people with psychiatric conditions. The objective was to examine, through popular music, the subcultural representations of crime and violence in the context of mental disorders, focusing on depictions of victims and offenders.
Background: There is a need to address the implementation of technological innovation into emergency medical services to facilitate and improve information exchange between prehospital emergency care providers, command centers, and hospitals during major incidents to enable better allocation of resources and minimize loss of life. At present, there is a lack of technology supporting real-time information sharing in managing major incidents to optimize the use of resources available.
Objective: The aim of this protocol is to develop, design, and evaluate information technology innovations for use in medical response to major incidents.
