Deceptive text detection using continuous semantic space models

We identify deceptive text by using different kinds of features: A continuous semantic space model based on latent Dirichlet allocation topics (LDA), one-hot representation (OHR), syntactic information from syntactic n-grams (SN), and lexicon-based features using the linguistic inquiry and word count dictionary (LIWC). Several combinations of these features were tested to assess the best source(s) for deceptive text identification. By selecting the appropriate features, we were able to obtain a benchmark-level performance using a Naïve Bayes classifier. We tested on three different available corpora: A corpus consisting of 800 reviews about hotels, a corpus consisting of 600 reviews about controversial topics, and a corpus consisting of 236 book reviews. We found that the merge of both LDA features and OHR yielded the best results, obtaining accuracy above 80% in all tested datasets. Additionally, this combination of features has the advantage that language-specific-resources are not required (e.g. SN, LIWC), compared to other reference works. Additionally, we present an analysis on which features lead to either deceptive or truthful texts, finding that certain words can play different roles (sometimes even opposing ones) depending on the task being evaluated.