Data Augmentation for Fake Reviews Detection (2023)

Abstract: In this research, we studied the relationship between data augmentation and model accuracy for the task of fake review detection. We used data generation methods to augment two different fake review datasets and compared the performance of models trained with the original data and with the augmented data. Our results show that the accuracy of our fake review detection model can be improved by 0.31 percentage points on DeRev Test and by 7.65 percentage points on Amazon Test by using the augmented datasets.

## introduction online communication has increased the speed and quantity of information sharing between people. while this change has brought a number of benefits, it also increased the opportunities for unscrupulous individuals to deceive @xcite @xcite . research shows that on average people tell 1 or 2 lies a day; now, lying has migrated from face-to-face communication to online @xcite . fake reviews are a particularly problematic type of deceptive online communication, given our reliance on online reviews to guide our purchases @xcite @xcite . about 1% to 6% positive hotel reviews are estimated to be fake @xcite . automatic deception detection methods rely on stylometric methods extracting from text hundreds of linguistic features @xcite @xcite . more recently, deep learning has been used @xcite @xcite . this research also led to the creation of several datasets for training such models @xcite @xcite . however, these datasets have a number of limitations. they tend to be small, and very domain dependent (a dataset of tripadvisor reviews is not suitable for training models to detect fake reviews on @xcite . even more crucially, few of them consist of genuine fake reviews; most were artificially created using crowdsourcing. but crowdsourced fake reviews are known to be different from genuine fake reviews @xcite . in this paper, we focus on the issue of creating suitable datasets for fake review detection research. data augmentation techniques using text generation would appear to be a potential solution to the problem of generating datasets for fake review detection when we only have a small amount of fake or genuine reviews. and given that modern text generation methods appear to be able to create artificial texts extremely similar to model texts used to prompt them, these methods might be more likely than crowdsourcing of creating artificial fake reviews similar to real fake reviews. in proposals such as @xcite , generators were used to augment data to improve discriminator performance. @xcite firstly uses the gpt-2 model to expand the existing data to obtain a larger data set and then applies the new data set to fake news detection. however, the amazon dataset used by salminen et al is very noisy, as discussed below. in this paper, we discuss a study based on the hypothesis that data augmentation can improve the performance of deception detectors. we followed an approach similar to @xcite but also used the cleaner dataset of amazon reviews introduced by @xcite and present evidence that the performance of a fake review detector can be improved by augmenting an existing dataset with artificially generated reviews. using our augmented datasets we achieved 0.31 and 7.65 percentage points improvements on derev test and amazon test respectively. ## experimental design in this section, we discuss the datasets and the generator and classifier models we used. ## experiments we ran two series of experiments. in the first series, a part of the derev dataset is used for testing. in the other series, a part of the amazon dataset is used for testing. in both series, the difference between experiments is which combination of datasets is used for training. only book reviews were used in our experiments, as this is the domain of the reviews in derev. ## conclusion our experimental results show that the robertabased classifier model achieves 0.31% and 7.65% accuracy improvements on the derev and amazon test sets, respectively. this shows that the accuracy of the classifier model can be improved to a certain extent by adding generated data. but our current experiments are limited to a single language and single domain. in future work, we plan to apply our data augmentation method to multiple languages and domains.