This article analyzes the emergence of nationalist martyr narratives and their dissemination via new media as forces for social mobilization and political change. Situating them in the religio-historical contexts of North Africa, we trace martyr narratives in Tunisia and Egypt back to pre-Islamic periods and compare them to the contemporary stories of Mohamed Bouazizi and Khaled Saeed. This reveals the impact of new media on the region, evident in ''virtual reliquaries,'' and the role that martyr narratives play as catalysts in social mobilization. The trajectory of the martyr narrative from the traditional religious context to the state-driven concept of civil religion allows for the political dimension of narratives resident within the religious context to surface in the contemporary discursive moment.
Concussion among athletes is an issue of growing concern, with efforts underway to improve detection, diagnosis, and treatment. Success depends on communication by athletes, as brain-related symptoms are often not outwardly visible. Education programs to increase reporting behavior have not been successful to date. In accordance with the socioecological approach to health, we argue that multiple levels of influence on student athletes must be addressed, and report a multi-dimensional, mixed-methods research project conducted to identify possible points of intervention into changing the culture of concussion-injury reporting among collegiate athletes. Using quantitative, qualitative and interpretive methods, we examine the individual-level
vested interests
athletes have in reporting or not reporting concussion symptoms, and how these interests interact with community-level
team culture
and
interpersonal relationships
, and social-level
cultural narratives
to influence concussion-reporting behavior. Our findings confirm the viability of this approach, identifying immediacy, separation of responsibility and pain-enduring story systems as particularly salient elements. We conclude that competing performance versus safety value structures, reflected in cultural narratives and team culture, create mixed-messages for athletes, which are resolved in favor of performance because athletes perceive concussion injuries to be of low immediacy.
Athlete safety and concussion injury have garnered considerable attention recently, and appropriate evaluation of athletes following head impacts depends, in part, on athletes’ self-reporting of the symptoms. The National Collegiate Athletic Association has focused primarily on concussion injury education to encourage self-reporting; however, such efforts have not been especially effective and many potential injuries continue to go unreported. This research investigates cultural narratives, derived from sports media and popular culture, and how their narrative logics contribute to the context in which student-athletes make head injury reporting decisions and how these narratives offer templates for understanding potential consequences. We argue that performance-oriented narratives are more prevalent and showcase pathways to more immediate satisfaction of desires or goals. Ultimately, we argue that not only does analysis of prevailing cultural narratives illuminate the context in which athletes make reporting decisions but also that such understanding could inform narrative-based interventions in order to emphasize and model recommended behaviors, such as injury reporting, and values, such as long-term brain health and player wellness.
Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. Given the rapidly evolving nature of news events and the limited amount of annotated data, state-of-the-art systems on fake news detection face challenges due to the lack of large numbers of annotated training instances that are hard to come by for early detection. In this work, we exploit multiple weak signals from different sources given by user and content engagements (referred to as weak social supervision), and their complementary utilities to detect fake news. We jointly leverage limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.3 https://bit.ly/39zPnMd
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