# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 2 Related Study
### Fake Reviews and Their Propagation  
As defined in [20], fake reviews are written by individuals who are not genuine customers of the product or service. In some cases, the individuals may even be incentivized through rewards to leave feedback. The underlying goal is to skew the public perception of a product or service. Such deceptive practices challenge the integrity of online review systems that have become very important across industries like e-commerce and hospitality. Initially, the intention was to provide transparency and guide potential consumers by offering genuine user feedback. But these platforms are now being increasingly exploited to manipulate public opinion. The goal of this is either to unfairly elevate or degrade the reputation of the offering. According to [21], the economic repercussions of such review manipulations are substantial. Their research indicates that even minor differences in a product's review scores can have significant impacts on consumer behavior. This, in turn, impacts the success or failure of a business. Even though fake reviews try to copy real reviews, they still can be distinguished. A study conducted in 2011 [22] showed that fake reviews tend to exhibit distinct linguistic patterns. These patterns can help differentiate them from genuine reviews upon closer examination. Fake reviews can be of two types: paid human-written content or bot-generated reviews. Researchers have [23] identified unique patterns in review metadata that could help detect fake reviews. Metadata such as timing and frequency of posts could indicate fraudulent activity. Having stronger verification systems on online platforms can help reduce the number of fake reviews. Various methods have been created to identify and minimize the impact of fake reviews. These methods range from simple rule-based algorithms to advanced machine learning (ML) techniques. Earlier methods were based on automated rules that looked for specific keywords and patterns in reviews. However, these initial methods often struggled with accuracy and scalability issues. As pointed out in [23], the deceptive practices used in review systems are complex. They require more sophisticated detection strategies. These strategies must be able to adapt as the tactics employed by creators of fake reviews continue to evolve and change over time. Rigid, rule-based approaches quickly became outdated against the dynamic nature of opinion spam and manipulation. In addition to traditional ML techniques, deep learning approaches have become popular for detecting fake reviews. They can capture subtle details in text through layers of neural networks. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have proven particularly effective. These models excel in analyzing semantic and syntactic structures in review texts to identify inconsistencies typical of fake reviews. In [24], they demonstrated the use of deep learning models that outperform traditional machine learning models. They integrated contextual embeddings that capture more complex dependencies in text data. Although ABSA has been widely used for sentiment analysis, its application in identifying fake reviews has only recently been explored. A recent study [25] introduced a new approach that applies ABSA to detect fake reviews. They propose using aspect replication instead of full-text replication for detecting fake reviews. The method is based on the observation that spammers often lack detailed knowledge about the product or service. They tend to manipulate the sentiment of specific aspects rather than the entire content. With their approach, they can distinguish how real and fake reviews are constructed. They employ a hybrid neural network model, combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. A survey that presents a taxonomy and covers advancements in three categories, fake review detection, fake reviewer detection, and fake review analysis, is [26].