SleuthTalk: Addressing the Last-Mile Problem in Historical Person Identification with Privacy, Collaboration, and Structured Feedback

Abstract

Identifying people in historical photographs is an important task in many fields, including history, journalism, genealogy, and collecting. A wide variety of different methods, such as manual analysis, facial recognition, and crowdsourcing, have been used to identify the unknown photos. However, because of the large numbers of candidates and the poor quality or lack of source evidence, accurate historical person identification still remains challenging. Researchers especially struggle with the ``last mile problem" of historical person identification, where they must make a selection among a small number of highly similar candidates. Collaboration, including both human-AI collaboration and collaboration within human teams, has shown the advantages of improving data accuracy, but there is lack of research about how we can design a collaborative workspace to support the historical person identification. In this work, we present SleuthTalk, a web-based collaboration tool integrated into the public website Civil War Photo Sleuth which addresses the last-mile problem in historical person identification by providing support for shortlisting potential candidates from face recognition results, private collaborative workspaces, and structured feedback interfaces. We evaluated this feature in a mixed-method study involving 6 participants, who spent one week each using SleuthTalk and a comparable social media platform to identify an unknown photo. The results of this study show how our design helps with identifying historical photos in a collaborative way and suggests directions for improvement in future work.