Finding light in dark archives: using AI to connect context and content in email

Stephanie Decker, David Kirsch, Santhilata Kuppili Venkata, Adam Nix

Research output: Contribution to journalArticlepeer-review


Email archives are important historical resources, but access to such data poses a unique archival challenge and many born-digital collections remain dark, while questions of how they should be effectively made available remain. This paper contributes to the growing interest in preserving access to email by addressing the needs of users, in readiness for when such collections become more widely available. We argue that for the content of email to be meaningfully accessed, the context of email must form part of this access. In exploring this idea, we focus on discovery within large, multi-custodian archives of organisational email, where emails’ network features are particularly apparent. We introduce our prototype search tool, which uses AI-based methods to support user-driven exploration of email. Specifically, we integrate two distinct AI models that generate systematically different types of results, one based upon simple, phrase-matching and the other upon more complex, BERT embeddings. Together, these provide a new pathway to contextual discovery that accounts for the diversity of future archival users, their interests and level of experience.
Original languageEnglish
JournalAI & Society
Early online date31 Dec 2021
Publication statusE-pub ahead of print - 31 Dec 2021


  • Email archives
  • Born-digital collections
  • Computational archival studies
  • Contextual email discovery


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