Checkpoint : A case study of a verification project during the 2019 Indian election

This thesis examines the Checkpoint research project and verification initiative that was introduced to address misinformation in private messaging applications during the 2019 Indian general election. Over two months, throughout the seven phases of the election, a team of analysts verified election related misinformation spread on the closed messaging network WhatsApp. Building on new automated technology, the project introduced a WhatsApp tipline which allowed users of the application to submit content to a team of analysts that verified user-generated content in an unprecedented way. The thesis presents a detailed ethnographic account of the implementation of the verification project. Ethnographic fieldwork has been combined with a series of semi-structured interviews in which analysts are underlining the challenges they faced throughout the project. Among the challenges, this study found that India’s legal framework limited the scope of the project so that the organisers had to change approach from an editorial project to one that was research based. Another problem touched the methodology of verification. Analysts perceived the use of online verification tools as a limiting factor when verifying content, as they experienced a need for more traditional journalistic verification methods. Technology was also a limiting factor. The tipline was quickly flooded with verification requests, the majority of which were unverifiable, and the team had to sort the queries manually. Existing technology such as image match check could be further implemented to deal more efficiently with multiple queries in future projects