Authorship Attribution and Gender Identification in Greek Blogs

The aim of this study is to obtain authorship attribution and author’s gender identification in a corpus of blogs written in Modern Greek language. More specifically, the corpus used contains 20 bloggers equally divided by gender (10 males & 10 females) with 50 blog posts from each author (1,000 posts in total and an overall size of 406,460 words). From this corpus we calculated a number of standard stylometric variables (e.g. word length statistics and various vocabulary “richness” indices) and 300 most frequent word and character n-grams (character and word unigrams, bigrams, trigrams). Support Vector Machines (SVM) were trained on this data, and the author’s gender prediction accuracy in 10-fold cross-validation experiment reached 82.6% accuracy, a result that is comparable to current state-of-the-art author profiling systems. Authorship attribution accuracy reached 85.4%, an equally satisfying result given the large number of candidate authors (n=20).

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