3,066 research outputs found

    Toward Designing Effective Warning Labels for Health Misinformation on Social Media

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    Health misinformation on social media has become a major threat to users. To alleviate this issue, platforms such as Twitter have started labeling posts considered as misinformation to warn users. However, the effectiveness of such labels on user perceptions and actions are not clear, as it has not yet been examined by researchers in prior studies. We aim to address this gap through a model, which draws upon concepts from color theory and construal level theory and focuses on the impact of three misinformation label characteristics: background color of the label, abstractness of the message, and assertiveness of the message language. We propose that the effectiveness of these warning labels will lead users to verify, avoid using, and avoid sharing such labeled posts on social media. This paper provides important theoretical contributions and aids policymakers and platform providers by offering insights on what motivates users to take protective actions

    Risk as affect:the affect heuristic in cybersecurity

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    Risk perception is an important driver of netizens’ (Internet users’) cybersecurity behaviours, with a number of factors influencing its formation. It has been argued that the affect heuristic can be a source of variation in generic risk perception. However, a major shortcoming of the supporting research evidence for this assertion is that the central construct, affect, has not been measured or analysed. Moreover, its influence in the cybersecurity domain has not yet been tested. The contribution of the research reported in this paper is thus, firstly, to test the affect heuristic while measuring its three constructs: affect, perceived risk and perceived benefit and, secondly, to test its impact in the cybersecurity domain. By means of two carefully designed studies (N = 63 and N = 233), we provide evidence for the influence of the affect heuristic on risk perception in the cybersecurity domain. We conclude by identifying directions for future research into the role of affect and its impact on cybersecurity risk perception

    Exploring the influence of message framing and image valence on the effectiveness of anti-speeding posters

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    A thesis submitted to the Institute for Applied Social Research, University of Bedfordshire, in fulfilment of requirements for the MSc by ResearchRoad safety advertisements that generate emotions have been acknowledged to increase the potential persuasiveness of an advertisement message. Nonetheless, there has been much debate about which message framing and image valence strategy is the most robust and influential persuader. In the current study, 40 UK vehicle users completed a simulated driving experiment and a series of self-report measures exploring the influence of three different types of anti-speeding advertisements: a negative loss-framed poster accompanied with a negative valence image, a positive gain-framed poster paired with a positive valence image, and a neutral anti-speeding poster. No significant differences were found between the three different types of anti-speeding advertisements and participants’ visual attention, memory or speeding behaviour. The results, however, showed that the negative anti-speeding advertisement was rated as significantly more effective in its ability to convince both other vehicle users and the vehicle user themselves to adhere to the legal speed limit. The influence of the differential advertisement strategies also appeared to fluctuate depending on several distinct factors and the disposition of the vehicle user. These findings suggest that emotionally-laden anti-speeding advertisements based on theoretical frameworks may effectively reduce the likelihood for participants to engage in risky driving behaviours and increase vehicle users’ intentions to adhere to the legal speed limit

    NEUVis: Comparing Affective and Effective Visualisation

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    Data visualisations are useful for providing insight from complex scientific data. However, even with visualisation, scientific research is difficult for non-scientists to comprehend. When developed by designers in collaboration with scientists, data visualisation can be used to articulate scientific data in a way that non-experts can understand. Creating human-centred visualisations is a unique challenge, and there are no frameworks to support their design. In response, this thesis presents a practice-led study investigating design methods that can be used to develop Non-Expert User Visualisations (NEUVis), data visualisations for a general public, and the response that people have to different kinds of NEUVis. For this research, two groups of ten users participated in quantitative studies, informed by Yvonna Lincoln and Egon Guba’s method of Naturalistic Inquiry, which asked non-scientists to express their cognitive and emotional response to NEUVis using different media. The three different types of visualisations were infographics, 3D animations and an interactive installation. The installation used in the study, entitled 18S rDNA, was developed and evaluated as part of this research using John Zimmerman’s Research Through Design methodology. 18S rDNA embodies the knowledge and design methods that were developed for this research, and provided an opportunity for explication of the entire NEUVis design process. The research findings indicate that developing visualisations for the non-expert audience requires a new process, different to the way scientists visualise data. The result of this research describes how creative practitioners collaborate with primary researchers and presents a new human-centred design thinking model for NEUVis. This model includes two design tools. The first tool helps designers merge user needs with data they wish to visualise. The second tool helps designers take that merged information and begin an iterative, user-centred design process
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