# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 1 Introduction  
Online reviews critically shape consumer purchasing behavior and brand reputation, serving as trusted decision-making tools. However, the surge in fake reviews on online platforms and social networks erodes this trust, creating challenges for buyers and businesses. These fake reviews distort consumer expectations about products and services, driving the development of detection methods. Current techniques predominantly rely on metadata, such as user online activity patterns or purchase history, which faces practical limitations due to restricted accessibility. Most graph-based methods for fake review detection model the reviews as nodes and solve a node classification problem [1] or an unsupervised problem [2]. This creates a pressing need for text content analysis, and identifying fake reviews through textual patterns alone remains an interesting challenge. Aspect-based sentiment analysis (ABSA) [3] addresses this gap by extending traditional sentiment analysis. Rather than assessing overall positive/negative sentiments, ABSA evaluates opinions about specific product attributes (e.g., \"Excellent battery life but unusable camera\"). This granular approach exposes inconsistencies--like contradictory assessments of related features or exaggerated praise for minor aspects that often signal fake generated reviews. By analyzing sentiment at the attribute level rather than depending on external metadata, ABSA provides a more viable approach for detecting fake online reviews. This paper presents a new method called FakeDetectionGCN for detecting fake reviews. Each review is represented as a graph, and the main idea is to perform graph classification (real or fake) using graph convolutional networks (GCNs) [4]. This approach is effective because it captures and analyzes the complex relationships between words within a review. We model each review as a graph, where nodes represent words and edges represent semantic relationships. A review may contain several aspect terms and sentiments. We aim to leverage ABSA to capture these aspect-sentiment relationships in our graph structure and improve the fake review detection process by discovering inconsistencies found between aspects and their sentiments in a review. Here is an example of a review: \"The food is delicious, but the service is slow\". The aspects are food and service. The sentiments are delicious (positive) and slow (negative). Figure 1 illustrates a simple example of how a review can be modeled as a graph. The aspect nodes are represented in blue and the sentiment words are in green. The edges connecting these nodes are weighted according to the sentiment. The basic steps of FakeDetectionGCN are shown in Figure 2. We begin with data gathering and preprocessing to prepare the reviews for analysis. Following this, we perform feature extraction using various word embeddings, such as GloVe [5], BERT [6], and Word2 Vec [7] for each review (graph). These embeddings capture the semantic details of the text. These are then used as the feature matrix for our GCN. We then create an adjacency matrix, where edge weights are adjusted based on the sentiment scores of the aspects. The next phase involves training the graph convolutional network (GCN) model. The GCN model learns from the node features and the aggregated neighborhood information. The model is then evaluated based on its ability to accurately classify reviews as genuine or fake. Additionally, hyper-parameters are fine-tuned to optimize performance. This paper is structured as follows: Section 2 reviews existing research on aspect-based sentiment analysis (ABSA) and the detection of fake reviews. Section 3 explains the background concepts that are used for the experiments. Section 4 describes the methodology used in the study. Section 5 consists of detailed results of the experiments and their outcomes, and Section 6 includes a comparative analysis. The report concludes with Section 7, which summarizes the findings and proposes ideas for future enhancements.