With the growth of technology that produces misinformation, there is a growing need to help users identify emerging types of fake media such as edited images and manipulated videos. In this work, we conduct a mixed-methods investigation into how we can provide provenance indicators to assist users in detecting newer forms of fake media. Specifically, we interview users regarding their experiences with different misinformation modes (text, image, video) to inform the design and content of indicators for previously unexplored media, especially fake videos. We find that media provenance \u2013 the source of the information \u2013 is a key heuristic used to evaluate all forms of fake media, and a heuristic that can be addressed by emerging technology. Thus, we subsequently design and investigate the use of provenance indicators to help users identify fake videos. We conduct a participatory design study to develop and design provenance indicators and evaluate participant-designed indicators via both expert evaluations and quantitative surveys (n=1,456) with end-users. Our results provide concrete design guidelines for the emerging issue of fake media. Our findings also raise concerns regarding users\u2019 tendency to overgeneralize indicators used to assist users in identifying misinformation, suggesting the need for further research on warning design in the ongoing fight against misinformation.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the growth of technology that produces misinformation, there is a growing need to help users identify emerging types of fake media such as edited images and manipulated videos. In this work, we conduct a mixed-methods investigation into how we can provide provenance indicators to assist users in detecting newer forms of fake media. 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