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 contribution

    2 Citations (Scopus)

    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 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 α1% (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.

    Original languageEnglish
    Title of host publicationComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings
    EditorsJavier Lopez, Jianying Zhou, Miguel Soriano
    PublisherSpringer-Verlag
    Pages132-151
    Number of pages20
    ISBN (Print)9783319990729
    DOIs
    Publication statusPublished - 2018 Jan 1
    Event23rd European Symposium on Research in Computer Security, ESORICS 2018 - Barcelona, Spain
    Duration: 2018 Sep 32018 Sep 7

    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

    Other

    Other23rd European Symposium on Research in Computer Security, ESORICS 2018
    CountrySpain
    CityBarcelona
    Period18/9/318/9/7

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    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Juuti, M., Sun, B., Mori, T., & Asokan, N. (2018). Stay on-topic: Generating context-specific fake restaurant reviews. In J. Lopez, J. Zhou, & M. Soriano (Eds.), 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). Springer-Verlag. https://doi.org/10.1007/978-3-319-99073-6_7