The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of quality assurance. In that context, we check the credibility of news content using selected linguistic features. We present two empirical studies to evaluate the usability of graphical interfaces that offer such credibility assessment. In a moderated qualitative interview with six participants, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. A subsequent quantitative online survey with 50 participants reveals a conflict between transparency and conciseness in the interface design, as well as a perceived hierarchy of metadata: the authorship of a news text is more important than the authorship of the credibility algorithm used to assess the content quality. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment.
翻译:互联网上关于COVID-19的信息传播速度越来越快,这要求采取自动质量保证措施。在这方面,我们利用选定的语言特征检查新闻内容的可信度。我们提出两项经验性研究,以评价提供这种可信度评估的图形界面的可用性。在与6名参与者的缓和定性访谈中,我们确定评级尺度、次级标准和算法作者是可用性的重要预测者。随后进行的有50名参与者的定量在线调查揭示了界面设计透明度和简洁性之间的冲突,以及所认知的元数据等级:新闻文本的作者比用于评估内容质量的可信度算法的作者更重要。最后,我们建议今后的研究,例如积极主动地记录用于语言处理和语言技术服务的与可信度有关的元数据,以及建立明确的可使用性预测的等级分类,用于自动可信度评估。