  Identifying misinformation is increasingly being recognized as an important
computational task with high potential social impact. Misinformation and fake
contents are injected into almost every domain of news including politics,
health, science, business, etc., among which, the fakeness in health domain
pose serious adverse effects to scare or harm the society. Misinformation
contains scientific claims or content from social media exaggerated with strong
emotion content to attract eyeballs. In this paper, we consider the utility of
the affective character of news articles for fake news identification in the
health domain and present evidence that emotion cognizant representations are
significantly more suited for the task. We outline a technique to leverage
emotion intensity lexicons to develop emotionized text representations, and
evaluate the utility of such a representation for identifying fake news
relating to health in various supervised and unsupervised scenarios. The
consistent and significant empirical gains that we observe over a range of
technique types and parameter settings establish the utility of the emotional
information in news articles, an often overlooked aspect, for the task of
misinformation identification in the health domain.
