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Design Exploration of Fake News: A Transdisciplinary Methodological Approach to Understanding Content Sharing and Trust on Social Media

机译:虚假新闻的设计探索:一种跨学科的方法论方法,用于理解社交媒体上的内容共享和信任

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This work in progress identifies some of the current research work in the areas of fake news, trust and social media. Algorithmic approaches and fact-checking tools are frequently used to help identify fake news sources and influences. Little work has been done on the influences of user experience (aesthetics, interface design, usability) in how end users engage with and recognize news. Standardized UX instruments such as SUPR-Q capture data on Trust, Loyalty and Appearance, as well as usability. UX approaches such as concurrent think aloud and eye tracking could allow for richer data and in-depth exploration of user behavior patterns in their social media use and sharing of news. We thus recommend a transdisciplinary approach to researching fake news that takes into account algorithmic approaches, psychometric data, and qualitative explorations of user behavior.
机译:这项正在进行的工作确定了假新闻,信任和社交媒体领域的一些当前研究工作。算法方法和事实检查工具经常用于帮助识别假新闻来源和影响。在最终用户如何接触和识别新闻方面,关于用户体验(美学,界面设计,可用性)的影响所做的工作很少。诸如SUPR-Q之类的标准化UX工具可获取有关信任,忠诚度和外观以及可用性的数据。诸如并发大声思考和眼睛跟踪之类的UX方法可以允许使用更丰富的数据,并深入探索用户在社交媒体使用和新闻共享中的行为模式。因此,我们建议采用跨学科的方法来研究假新闻,其中应考虑算法方法,心理数据以及对用户行为的定性探索。

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