Identifying and preventing fraudulent responses in online public health surveys: Lessons learned during the COVID-19 pandemic

Web-based survey data collection has become increasingly popular, and limitations on in-person data collection during the COVID-19 pandemic have fueled this growth. However, the anonymity of the online environment increases the risk of fraud, which pose major risks to data integrity. As part of a study of COVID-19 and the return to in-person school, we implemented a web-based survey of parents in Maryland, USA, between December 2021 and July 2022. Recruitment relied, in part, on social media advertisements. Despite implementing many existing best practices, the survey was challenged by sophisticated fraudsters. In this paper, we describe efforts to identify and prevent fraudulent online survey responses and provide specific, actionable recommendations for identifying and preventing online survey fraud. Some strategies can be deployed within the web-based data collection platform such as Internet Protocol address logging to identify duplicate responses and comparison of client-side and server-side time stamps to identify responses that may have been completed by respondents outside of the surveys target geography. Additional approaches include the use of a 2-stage survey design, repeated within-survey and cross-survey validation questions, the addition of "speed bump" questions to thwart careless or computerized responders, and the use of optional open-ended survey responses to identify irrelevant responses. We describe best practices for ongoing survey data review and verification, including algorithms to simplify aspects of this review.