  In digital environments where substantial amounts of information are shared
online, news headlines play essential roles in the selection and diffusion of
news articles. Some news articles attract audience attention by showing
exaggerated or misleading headlines. This study addresses the \textit{headline
incongruity} problem, in which a news headline makes claims that are either
unrelated or opposite to the contents of the corresponding article. We present
\textit{BaitWatcher}, which is a lightweight web interface that guides readers
in estimating the likelihood of incongruence in news articles before clicking
on the headlines. BaitWatcher utilizes a hierarchical recurrent encoder that
efficiently learns complex textual representations of a news headline and its
associated body text. For training the model, we construct a million scale
dataset of news articles, which we also release for broader research use. Based
on the results of a focus group interview, we discuss the importance of
developing an interpretable AI agent for the design of a better interface for
mitigating the effects of online misinformation.
