It is challenging to control the quality of online information due to the lack of supervision. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data. However, extracting features is time-consuming and not a highly effective process. This study proposes the BERT-based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, different language models (LM) ’ performance with different trainable parameters was compared. The proposed technique is tested on different short and long text datasets. The result of the proposed technique has been compared with the state-of-the-art methods on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. We also tested the proposed technique by combining the datasets. The results demonstrated that the large training and testing size of data considerably improves the technique’s performance. Therefore, it is suggested that the dataset, splitting data, and classification techniques must be considered carefully to analyse performance of the solutions.