Identifying Deceptive Review Comments with Rumor and Lie Theories

Lin, Chia Hsun; Hsu, Ping-Yu; Cheng, Ming Shien; Lei, Hong Tsuen; Hsu, Ming Chia

doi:10.1007/978-3-319-61833-3_44

Cited by 4 publications

(3 citation statements)

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“…The evolution of text mining and natural language processing algorithms has facilitated the extraction of information from the textual component of online reviews (McGuire, 2017). Recently, the number of published studies taking advantage of the textual component of reviews has increased, focusing on issues such as identifying relevant topics mentioned in reviews (Calheiros et al, 2017), understanding what satisfied and unsatisfied customers mention (Berezina et al, 2016;Xu et al, 2017), assessing the impact of social media on a hotel's service (Duan et al, 2016), understanding what guests think of hotels (Han et al, 2016;He et al, 2017;Xiang et al, 2015;Xu and Li, 2016), examining the consumers' prepurchase decisions (Noone and McGuire, 2014), identifying deceptive review comments (Lin et al, 2017), and review opinion classification predictions (Bjørkelund et al, 2012;Salehan and Kim, 2016). Although some of these works resort to sentiment analysis and machine learning, to the extent of the authors knowledge, only three of them use these tools to predict review ratings (i.e., Ganu et al, 2013;Lei and Qian, 2015;López Barbosa et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Hotel online reviews: creating a multi-source aggregated index

António¹,

Almeida

Nunes

et al. 2018

IJCHM

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Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

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Section: Introductionmentioning

confidence: 99%

Hotel online reviews: creating a multi-source aggregated index

António¹,

Almeida

Nunes

et al. 2018

IJCHM

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“…A model for fake review identification was presented by Cao et al [10], emphasizing multi-feature learning and independent training for classification. By examining phony review comments using the prism of rumor and lie theories, Lin et al [11] advanced their theoretical understanding of the topic. Pascucci et al [12] created a tool for detecting fraudulent reviews in the hotel industry using computational stylometry.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Truthful and Deceptive Hotel Reviews

Gupta,

Bhargava,

Agarwal

et al. 2024

Sustainability

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For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

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“…Ott et al collected truthful reviews online and completed the data set with false crowdsourced texts. Their corpus has been extensively used by researchers including Feng et al (2012), Banerjee and Chua (2014), Hernández Fusilier et al (2015), and Lin et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Fake opinion detection: how similar are crowdsourced datasets to real data?

Fornaciari

Cagnina

Rosso

et al. 2020

Lang Resources & Evaluation

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Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is difficult, because normally it is not possible to know whether reviews are genuine. A common workaround involves collecting (supposedly) truthful reviews online and adding them to a set of deceptive reviews obtained through crowdsourcing services. Models trained this way are generally successful at discriminating between 'genuine' online reviews and the crowdsourced deceptive reviews. It has been argued that the deceptive reviews obtained via crowdsourcing are very different from real fake reviews, but the claim has never been properly tested.In this paper, we compare (false) crowdsourced reviews with a set of 'real' fake reviews published on line. We evaluate their degree of similarity and their usefulness in training models for the detection of untrustworthy reviews. We find that the deceptive reviews collected via crowdsourcing are significantly different from the fake reviews published online. In the case of the artificially produced deceptive texts, it turns out that their domain similarity with the targets affects the models' performance, much more than their untruthfulness. This suggests that the use of crowdsourced datasets for opinion spam detection may not result in models applicable to the real task of detecting deceptive reviews.As an alternative method to create large-size datasets for the fake reviews detection task, we propose methods based on the probabilistic annotation of unlabeled texts, relying on the use of meta-information generally available on the e-commerce sites. Such methods are independent from the content of the reviews and allow to train reliable models for the detection of fake reviews.

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Identifying Deceptive Review Comments with Rumor and Lie Theories

Cited by 4 publications

References 5 publications

Hotel online reviews: creating a multi-source aggregated index

Hotel online reviews: creating a multi-source aggregated index

Deep Learning-Based Truthful and Deceptive Hotel Reviews

Fake opinion detection: how similar are crowdsourced datasets to real data?

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