2013/05/01 by Marcelo Luiz Brocardo, Issa Traoré, Sherif Saad +1 · 103 citations
Computer Science · #Authorship Attribution and Profiling #Spam and Phishing Detection #Topic Modeling #Stylometry #Computer science #Writing style #USable #Natural language processing #Word error rate #Feature (linguistics) #Information retrieval #Identity (music) #Artificial intelligence #Authorship attribution #World Wide Web #Linguistics
paper · doi:10.1109/cits.2013.6705711
openalex publication_date 2013/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Authorship verification can be checked using stylometric techniques through the analysis of linguistic styles and writing characteristics of the authors. Stylometry is a behavioral feature that a person exhibits during writing and can be extracted and used potentially to check the identity of the author of online documents. Although stylometric techniques can achieve high accuracy rates for long documents, it is still challenging to identify an author for short documents, in particular when dealing with large authors populations. These hurdles must be addressed for stylometry to be usable in checking authorship of online messages such as emails, text messages, or twitter feeds. In this paper, we pose some steps toward achieving that goal by proposing a supervised learning technique combined with n-gram analysis for authorship verification in short texts. Experimental evaluation based on the Enron email dataset involving 87 authors yields very promising results consisting of an Equal Error Rate (EER) of 14.35% for message blocks of 500 characters.