In general, people are poorly protected against cyberthreats, with the main reason being user behaviour. For the study described in this paper, a ques-tionnaire was developed in order to understand how people’s knowledge of and attitude towards both cyberthreats and cyber security controls affect in-tention to adopt cybersecure behaviour. The study divides attitude into a cog-nitive and an affective component. Although only the cognitive component of attitude is usually studied, the results from a questionnaire of 300 respond-ents show that both the affective and cognitive components of attitude have a clearly positive, albeit varying, influence on behavioural intention, with the affective component having an even greater effect on attitude than the cog-nitive aspect. No correlation was found between knowledge and behavioural intention. The results indicate that attitude is an important factor to include when developing behavioural interventions, but also that different kinds of attitude should be addressed differently in interventions.
Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.
Post-partum hemorrhaging is a medical emergency that occurs during childbirth and, in extreme cases, can be life-threatening. It is the number one cause of maternal mortality worldwide. High-quality training of medical staff can contribute to early diagnosis and work towards preventing escalation towards more serious cases. Healthcare education uses manikin-based simulators to train obstetricians for various childbirth scenarios before training on real patients. However, these medical simulators lack certain key features portraying important symptoms and are incapable of communicating with the trainees. The authors present a digital embodiment agent that can improve the current state of the art by providing a specification of the requirements as well as an extensive design and development approach. This digital embodiment allows educators to respond and role-play as the patient in real time and can easily be integrated with existing training procedures. This research was performed in collaboration with medical experts, making a new contribution to medical training by bringing digital humans and the representation of affective interfaces to the field of healthcare.
