  It is challenging to control the quality of online information due to the
lack of supervision over all the information posted online. 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 techniques 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 data training and testing size considerably
improves the technique's performance.
