  Detecting deception in natural language has a wide variety of applications,
but because of its hidden nature there are currently no public, large-scale
sources of labeled deceptive text. This work introduces the Mafiascum dataset
[1], a collection of over 700 games of Mafia, in which players are randomly
assigned either deceptive or non-deceptive roles and then interact via forum
postings. Over 9000 documents were compiled from the dataset, which each
contained all messages written by a single player in a single game. This corpus
was used to construct a set of hand-picked linguistic features based on prior
deception research, as well as a set of average word vectors enriched with
subword information. A logistic regression classifier fit on a combination of
these feature sets achieved an average precision of 0.39 (chance = 0.26) and an
AUROC of 0.68 on 5000+ word documents. On 50+ word documents, an average
precision of 0.29 (chance = 0.23) and an AUROC of 0.59 was achieved.
  [1] https://bitbucket.org/bopjesvla/thesis/src
