  Widespread and rapid dissemination of false news has made fact-checking an
indispensable requirement. Given its time-consuming and labor-intensive nature,
the task calls for an automated support to meet the demand. In this paper, we
propose to leverage commonsense knowledge for the tasks of false news
classification and check-worthy claim detection. Arguing that commonsense
knowledge is a factor in human believability, we fine-tune the BERT language
model with a commonsense question answering task and the aforementioned tasks
in a multi-task learning environment. For predicting fine-grained false news
types, we compare the proposed fine-tuned model's performance with the false
news classification models on a public dataset as well as a newly collected
dataset. We compare the model's performance with the single-task BERT model and
a state-of-the-art check-worthy claim detection tool to evaluate the
check-worthy claim detection. Our experimental analysis demonstrates that
commonsense knowledge can improve performance in both tasks.
