Over the past several decades, advances in technology have significantly impacted all aspects of the financial system. While it has led to numerous benefits, it has also increased the methods for manipulating the market. A frequent platform used to perform these market manipulation schemes has been through social media. In particular, online forums have become a tool for manipulators to disseminate false or misleading information so that they can profit from other investors. As a result, my research provides investors with valuable insights and the tools necessary for detecting pump-and-dump schemes. To achieve this, posts and comments within financial forums were first collected. Then, financial data was added to associate the texts with resulting market behaviours. By using statistical methods, the records were then initially labelled depending on whether they exhibited a known market pattern that commonly occurs when investors act upon deceptive content. To further improve upon the labelling method, comments of deceptive posts were then relabelled based on their level of agreement to fraudulent information. With the described agreement model, results showed that predictions among the tested classification techniques (XGBoost, Random Forest, SVM, MLP, CNN, BiLSTM) were improved. Additionally, by comparing the performance of the classifiers, CNNs were found to be the best performing model among those that were tested.