Utilizing Latent Posting Style for Authorship Attribution on Short Texts

Character n-grams and word n-grams are the most widely used features for authorship attribution on short texts. In this paper, we propose a new method which exploits latent posting styles estimated from authors' short texts. The new posting style features characterize each user's posting style through sentiment orientation and post length. Concise hidden posting styles are captured by Latent Dirichlet Allocation (LDA), where we consider two types of LDA models. Then the vectors of latent posting styles are concatenated with averaged word embeddings of character n-grams and word n-grams, to be used to train a support vector machine. Our results show that combining latent posting styles with the traditional features can improve the accuracy of authorship attribution up to 5.2%.