Data Fusion for Better Fake Reviews Detection

Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding format, for fake reviews detection. We evaluate these representations with various state-of-theart deep learning models, such as a BILSTM, LSTM, GRU, CNN, and MLP. Following this, we propose to incorporate different data representations and classification models using early and late data fusion techniques in order to improve the prediction performance. The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp. The results demonstrate that a combination of different data representations significantly outperforms any single data representation.