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 contributionScientificpeer-review

4 Citations (Scopus)
118 Downloads (Pure)

Abstract

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
Pages132-151
Number of pages20
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Research in Computer Security - Barcelona, Spain
Duration: 3 Sep 20189 Sep 2018
Conference number: 23
https://esorics2018.upc.edu/

Publication series

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

Conference

ConferenceEuropean Symposium on Research in Computer Security
CountrySpain
CityBarcelona
Period03/09/201809/09/2018
Internet address

Keywords

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

Equipment

Science-IT

Mikko Hakala (Manager)

School of Science

Facility/equipment: Facility

  • Cite this

    Juuti, M., Sun, B., Mori, T., & Asokan, N. (2018). Stay On-Topic: Generating Context-specific Fake Restaurant Reviews. In Computer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings (pp. 132-151). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11098 LNCS). https://doi.org/10.1007/978-3-319-99073-6_7