@inproceedings{buscaldi-liyanage-2024-detecting,
title = "Detecting {AI}-enhanced Opinion Spambots: a study on {LLM}-generated Hotel Reviews",
author = "Buscaldi, Davide and
Liyanage, Vijini",
editor = "Malmasi, Shervin and
Fetahu, Besnik and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.ecnlp-1.8",
pages = "74--78",
abstract = "Opinion spamming is the posting of fake opinions or reviews to promote or discredit target products, services, or individuals. The concern surrounding this activity has grown steadily especially because of the development of automated bots for this purpose ({``}spambots{''}). Nowadays, Large Language Models (LLMs) have proved their ability to generate text that is almost indistinguishable from human-written text. Therefore, there is a growing concern regarding the use of these models for malicious purposes, among them opinion spamming. In this paper, we carry out a study on LLM-generated reviews, in particular hotel reviews as we chose the well-known Opinion Spam corpus by Myle Ott as the seed for our dataset. We generated a set of fake reviews with various models and applied different classification algorithms to verify how difficult is it to detect this kind of generated content. The results show that by providing enough training data, it is not difficult to detect the fake reviews generated by such models, as they tend to associate the aspects in the reviews with the same attributes.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="buscaldi-liyanage-2024-detecting">
<titleInfo>
<title>Detecting AI-enhanced Opinion Spambots: a study on LLM-generated Hotel Reviews</title>
</titleInfo>
<name type="personal">
<namePart type="given">Davide</namePart>
<namePart type="family">Buscaldi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vijini</namePart>
<namePart type="family">Liyanage</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shervin</namePart>
<namePart type="family">Malmasi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Besnik</namePart>
<namePart type="family">Fetahu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nicola</namePart>
<namePart type="family">Ueffing</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oleg</namePart>
<namePart type="family">Rokhlenko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eugene</namePart>
<namePart type="family">Agichtein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ido</namePart>
<namePart type="family">Guy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Opinion spamming is the posting of fake opinions or reviews to promote or discredit target products, services, or individuals. The concern surrounding this activity has grown steadily especially because of the development of automated bots for this purpose (“spambots”). Nowadays, Large Language Models (LLMs) have proved their ability to generate text that is almost indistinguishable from human-written text. Therefore, there is a growing concern regarding the use of these models for malicious purposes, among them opinion spamming. In this paper, we carry out a study on LLM-generated reviews, in particular hotel reviews as we chose the well-known Opinion Spam corpus by Myle Ott as the seed for our dataset. We generated a set of fake reviews with various models and applied different classification algorithms to verify how difficult is it to detect this kind of generated content. The results show that by providing enough training data, it is not difficult to detect the fake reviews generated by such models, as they tend to associate the aspects in the reviews with the same attributes.</abstract>
<identifier type="citekey">buscaldi-liyanage-2024-detecting</identifier>
<location>
<url>https://aclanthology.org/2024.ecnlp-1.8</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>74</start>
<end>78</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Detecting AI-enhanced Opinion Spambots: a study on LLM-generated Hotel Reviews
%A Buscaldi, Davide
%A Liyanage, Vijini
%Y Malmasi, Shervin
%Y Fetahu, Besnik
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F buscaldi-liyanage-2024-detecting
%X Opinion spamming is the posting of fake opinions or reviews to promote or discredit target products, services, or individuals. The concern surrounding this activity has grown steadily especially because of the development of automated bots for this purpose (“spambots”). Nowadays, Large Language Models (LLMs) have proved their ability to generate text that is almost indistinguishable from human-written text. Therefore, there is a growing concern regarding the use of these models for malicious purposes, among them opinion spamming. In this paper, we carry out a study on LLM-generated reviews, in particular hotel reviews as we chose the well-known Opinion Spam corpus by Myle Ott as the seed for our dataset. We generated a set of fake reviews with various models and applied different classification algorithms to verify how difficult is it to detect this kind of generated content. The results show that by providing enough training data, it is not difficult to detect the fake reviews generated by such models, as they tend to associate the aspects in the reviews with the same attributes.
%U https://aclanthology.org/2024.ecnlp-1.8
%P 74-78
Markdown (Informal)
[Detecting AI-enhanced Opinion Spambots: a study on LLM-generated Hotel Reviews](https://aclanthology.org/2024.ecnlp-1.8) (Buscaldi & Liyanage, ECNLP-WS 2024)
ACL