  Twitter has become one of the most sought after places to discuss a wide
variety of topics, including medically relevant issues such as cancer. This
helps spread awareness regarding the various causes, cures and prevention
methods of cancer. However, no proper analysis has been performed, which
discusses the validity of such claims. In this work, we aim to tackle the
misinformation spread in such platforms. We collect and present a dataset
regarding tweets which talk specifically about cancer and propose an
attention-based deep learning model for automated detection of misinformation
along with its spread. We then do a comparative analysis of the linguistic
variation in the text corresponding to misinformation and truth. This analysis
helps us gather relevant insights on various social aspects related to
misinformed tweets.
