  In recent years people have become increasingly reliant on social media to
read news and get information, and some social media users post unsubstantiated
information to gain attention. Such information is known as rumours. Nowadays,
rumour detection is receiving a growing amount of attention because of the
pandemic of the New Coronavirus, which has led to a large number of rumours
being spread. In this paper, a Natural Language Processing (NLP) system is
built to predict rumours. The best model is applied to the COVID-19 tweets to
conduct exploratory data analysis. The contribution of this study is twofold:
(1) to compare rumours and facts using state-of-the-art natural language
processing models in two dimensions: language structure and propagation route.
(2) An analysis of how rumours differ from facts in terms of their lexical use
and the emotions they imply. This study shows that linguistic structure is a
better feature to distinguish rumours from facts compared to the propagation
path. In addition, rumour tweets contain more vocabulary related to politics
and negative emotions.
