2001/12/01 by Olivier De Vel, Alison Anderson, Malcolm Corney +1 · 2 citations
Computer Science · #Authorship Attribution and Profiling #Text and Document Classification Technologies #Topic Modeling #Computer science #Identification (biology) #Set (abstract data type) #Focus (optics) #Information retrieval #Authorship attribution #Electronic mail #Data mining #World Wide Web #Natural language processing
paper · doi:10.1145/604264.604272
openalex publication_date 2001/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
We describe an investigation into e-mail content mining for author identification, or authorship attribution, for the purpose of forensic investigation. We focus our discussion on the ability to discriminate between authors for the case of both aggregated e-mail topics as well as across different e-mail topics. An extended set of e-mail document features including structural characteristics and linguistic patterns were derived and, together with a Support Vector Machine learning algorithm, were used for mining the e-mail content. Experiments using a number of e-mail documents generated by different authors on a set of topics gave promising results for both aggregated and multi-topic author categorisation.