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

Mika Juuti, Bo Sun, Tatsuya Mori, N. Asokan

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

17 Citations (Scopus)
314 Downloads (Pure)


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 skeptical 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% (Section 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.
Original languageEnglish
Title of host publicationComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings
Number of pages20
ISBN (Print)9783319990729
Publication statusPublished - 2018
MoE publication typeA4 Conference publication
EventEuropean Symposium on Research in Computer Security - Barcelona, Spain
Duration: 3 Sept 20187 Sept 2018
Conference number: 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11098 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Symposium on Research in Computer Security
Abbreviated titleESORICS
Internet address


  • security
  • Machine Learning
  • fraud detection
  • neural machine translation
  • social media
  • natural language


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