  The role of social media in opinion formation has far-reaching implications
in all spheres of society. Though social media provide platforms for expressing
news and views, it is hard to control the quality of posts due to the sheer
volumes of posts on platforms like Twitter and Facebook. Misinformation and
rumours have lasting effects on society, as they tend to influence people's
opinions and also may motivate people to act irrationally. It is therefore very
important to detect and remove rumours from these platforms. The only way to
prevent the spread of rumours is through automatic detection and classification
of social media posts. Our focus in this paper is the Twitter social medium, as
it is relatively easy to collect data from Twitter. The majority of previous
studies used supervised learning approaches to classify rumours on Twitter.
These approaches rely on feature extraction to obtain both content and context
features from the text of tweets to distinguish rumours and non-rumours.
Manually extracting features however is time-consuming considering the volume
of tweets. We propose a novel approach to deal with this problem by utilising
sentence embedding using BERT to identify rumours on Twitter, rather than the
usual feature extraction techniques. We use sentence embedding using BERT to
represent each tweet's sentences into a vector according to the contextual
meaning of the tweet. We classify those vectors into rumours or non-rumours by
using various supervised learning techniques. Our BERT based models improved
the accuracy by approximately 10% as compared to previous methods.
