Stay On-Topic: Generating Context-Specific Fake Restaurant Reviews

Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM) has one drawback: it has difficulties staying in context, i.e. when it generates a review for specific target entity, the resulting review may contain phrases that are unrelated to the target, thus increasing its detectability. In this work, we present and evaluate a more sophisticated technique based on neural machine translation (NMT) with which we can generate reviews that stay on-topic. We test multiple variants of our technique using native English speakers on Amazon Mechanical Turk. We demonstrate that reviews generated by the best variant have almost optimal undetectability (class-averaged F-score 47%). We conduct a user study with experienced users and show that our method evades detection more frequently compared to the state-of-the-art (average evasion 3.2 / 4 vs 1.5 / 4) with statistical significance, at level \(\alpha = 1\%\) (Sect. 4.3). We develop very effective detection tools and reach average F-score of \(97\%\) in classifying these. Although fake reviews are very effective in fooling people, effective automatic detection is still feasible.