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

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

Researchers

Research units

  • Japan National Institute of Information and Communications Technology
  • Waseda University
  • RIKEN Center for Advanced Intelligence Project

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.

Details

Original languageEnglish
Title of host publicationComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings
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

    Research areas

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

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