The public confidence and trust in online healthcare information have been greatly dented following the COVID-19 pandemic, which triggered a significant rise in online health misinformation. Existing literature shows that different datasets have been created to aid with detecting false information associated with this COVID infodemic. However, most of these datasets contain mostly unimodal data, which comprise primarily textual cues, and not visual cues, like images, infographics, and other graphic data components. Prior works point to the fact that there are only a handful of multimodal datasets that support COVID misinformation identification, and they lack an organized, processed and analyzed repository of visual cues. The novel CoVCues dataset, which represents a varied set of image artifacts, addresses this gap and advocates for the use of visual cues towards detecting online health misinformation. As part of validating the contents and utility of our CoVCues dataset, we have conducted a preliminary user assessment study, where different participants have been surveyed through a set of questionnaires to determine how effectively these dataset images contribute to the user perceived information reliability. These survey responses helped provide early insights into how different stakeholder groups interpret visual cues in the context of online health information and communication. The findings from this novel user assessment study offer valuable feedback for refining our CoVCues dataset and for supporting our claim that visual cues are underutilized but useful in combating the COVID infodemic. To our knowledge, this user assessment research study, as described in this paper, is the first of its kind work, involving COVID visual cues, that demonstrates the important role that our CoVCues dataset can potentially play in aiding COVID infodemic related future research work.

翻译：COVID-19大流行严重削弱了公众对在线医疗信息的信心与信任，同时引发了网络健康错误信息的显著增长。现有文献表明，已有多种数据集被创建用于辅助检测与此次COVID信息疫情相关的虚假信息。然而，这些数据集大多为单模态数据，主要包含文本线索，而缺乏图像、信息图表及其他图形数据组件等视觉线索。先前研究指出，目前仅存在少数支持COVID错误信息识别的多模态数据集，且它们缺乏系统化、经过处理和分析的视觉线索库。新颖的CoVCues数据集包含多样化的图像素材，填补了这一空白，并倡导利用视觉线索来检测在线健康错误信息。为验证CoVCues数据集的内容与实用性，我们开展了一项初步用户评估研究，通过系列问卷调查不同参与者，以评估这些数据集图像对用户感知信息可靠性的贡献程度。这些调查反馈为理解不同利益相关群体在在线健康信息传播背景下如何解读视觉线索提供了早期洞见。此项新型用户评估研究的发现为完善CoVCues数据集提供了宝贵反馈，并支持了我们关于视觉线索在应对COVID信息疫情中未被充分利用但具有重要价值的观点。据我们所知，本文所述的用户评估研究是首个涉及COVID视觉线索的同类工作，它证明了CoVCues数据集在辅助未来COVID信息疫情相关研究方面可能发挥的重要作用。