  Social networking sites, blogs, and online articles are instant sources of
news for internet users globally. However, in the absence of strict regulations
mandating the genuineness of every text on social media, it is probable that
some of these texts are fake news or rumours. Their deceptive nature and
ability to propagate instantly can have an adverse effect on society. This
necessitates the need for more effective detection of fake news and rumours on
the web. In this work, we annotate four fake news detection and rumour
detection datasets with their emotion class labels using transfer learning. We
show the correlation between the legitimacy of a text with its intrinsic
emotion for fake news and rumour detection, and prove that even within the same
emotion class, fake and real news are often represented differently, which can
be used for improved feature extraction. Based on this, we propose a multi-task
framework for fake news and rumour detection, predicting both the emotion and
legitimacy of the text. We train a variety of deep learning models in
single-task and multi-task settings for a more comprehensive comparison. We
further analyze the performance of our multi-task approach for fake news
detection in cross-domain settings to verify its efficacy for better
generalization across datasets, and to verify that emotions act as a
domain-independent feature. Experimental results verify that our multi-task
models consistently outperform their single-task counterparts in terms of
accuracy, precision, recall, and F1 score, both for in-domain and cross-domain
settings. We also qualitatively analyze the difference in performance in
single-task and multi-task learning models.
