Creating and detecting fake reviews of online products

Salminen, Joni; Kandpal, Chandrashekhar; Kamel, Ahmed; Jung, Sin‐Ho; Jansen, Bernard J.

doi:10.1016/j.jretconser.2021.102771

Cited by 134 publications

(83 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We hypothesize that increasing the data set size would help the classifier better separate pain point tweets from those not containing a pain point. This task would be better achieved with a classifier that has had robust performance in similar tasks (e.g., fake review detection; Salminen et al 2022), which is a technically similar problem (i.e., text classification with binary classes).…”

Section: Resultsmentioning

confidence: 99%

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Salminen¹,

Mustak²,

Corporan³

et al. 2022

Journal of Interactive Marketing

Self Cite

View full text Add to dashboard Cite

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.

show abstract

Section: Resultsmentioning

confidence: 99%

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Salminen¹,

Mustak²,

Corporan³

et al. 2022

Journal of Interactive Marketing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In extreme cases, they led to financial loss for companies and legal cases [56]. While traditionally, fake reviewers are written by humans, with the advancement of Natural Language Generation technology, it has been shown that fake reviews automatically generated by programs are even more difficult for human annotators to detect [57]. There is an extensive amount of studies on automated fake review detection and for that reason, we refer readers to the surveys by [54] while below we present a brief overview of this field, highlighting the novelty of our work.…”

Section: Fake Product Review Detectionmentioning

confidence: 99%

“…Thus similar to product classification, it involves extracting features of the review text (metadata), representing them in a machine processable format (feature representation), and training a model that is able to generalise patterns using the features and apply the patterns to unseen data (algorithm). In terms of features (metadata), [57] broadly categorised them into 'lexical' and 'non-lexical'. Lexical features are attributes derived from text, such as words, n-grams, punctuations and latent topics.…”

Section: Fake Product Review Detectionmentioning

confidence: 99%

“…In contrast, our work is the first that explores if a corpus of product details (instead of their reviews) can be used to learn word embeddings for this task. Compared to [57] who also used LMs, our work explores the effect of continued pre-training of LM using in-domain corpus, while [57] did not.…”

Section: Fake Product Review Detectionmentioning

confidence: 99%

“…We use the dataset from [57], which were created using a Natural Language Generation model and contains over 40,000 reviews of products from 10 broad categories (e.g. Books, Electronics), each labelled as either a fake review, or genuine.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

Zhang

Song

2022

SN COMPUT. SCI.

View full text Add to dashboard Cite

The Linked Open Data practice has led to a significant growth of structured data on the Web. While this has created an unprecedented opportunity for research in the field of Natural Language Processing, there is a lack of systematic studies on how such data can be used to support downstream NLP tasks. This work focuses on the e-commerce domain and explores how we can use such structured data to create language resources for product data mining tasks. To do so, we process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating language resources: training word-embedding models, continued pre-training of BERT-like language models, and training machine translation models that are used as a proxy to generate product-related keywords. These language resources are then evaluated in three downstream tasks, product classification, linking, and fake review detection using an extensive set of benchmarks. Our results show word embeddings to be the most reliable and consistent method to improve the accuracy on all tasks (with up to 6.9% points in macro-average F1 on some datasets). Contrary to some earlier studies that suggest a rather simple but effective approach such as building domain-specific language models by pre-training using in-domain corpora, our work serves a lesson that adapting these methods to new domains may not be as easy as it seems. We further analyse our datasets and reflect on how our findings can inform future research and practice.

show abstract

Incentivized fake reviews: When cognitive reappraisal paves the way for an immoral journey

Malik,

Sadeghi R,

Paswan

et al. 2024

J of Consumer Behaviour

View full text Add to dashboard Cite

Recent studies explain how to detect fake reviews and their impacts on consumers and businesses. Despite the prevalence and significance of fake reviews, prior research has yet to fully explore the motivations and psychological mechanisms behind voluntary participation in writing fake reviews, especially in incentivized campaigns. This paper explains why consumers engage in the immoral act of writing fake reviews in return for free products. We investigate the interactive role of individuals' self‐focused motivations and cognitive reappraisal ability in diminishing consumers' moral emotions of guilt, subsequently influencing their intention to write incentivized reviews. Built on three studies, the findings reveal that customers' self‐focused motivations coupled with their ability to regulate emotions dissuade their feelings of guilt. Consequently, customers who experience downregulated emotions of guilt show a higher intention to write incentivized fake reviews. This study contributes to the existing literature by illustrating the driving factors toward writing incentivized fake reviews, especially by depicting the negative role of cognitive reappraisal. The study also has managerial implications indicating that such customer behavior can be avoided by fostering a sense of consideration toward others by making them aware about the impact of their actions.

show abstract

Creating and detecting fake reviews of online products

Cited by 134 publications

References 42 publications

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

Incentivized fake reviews: When cognitive reappraisal paves the way for an immoral journey

Contact Info

Product

Resources

About