  In this paper, we consider a dataset comprising press releases about health
research from different universities in the UK along with a corresponding set
of news articles. First, we do an exploratory analysis to understand how the
basic information published in the scientific journals get exaggerated as they
are reported in these press releases or news articles. This initial analysis
shows that some news agencies exaggerate almost 60\% of the articles they
publish in the health domain; more than 50\% of the press releases from certain
universities are exaggerated; articles in topics like lifestyle and childhood
are heavily exaggerated. Motivated by the above observation we set the central
objective of this paper to investigate how exaggerated news spreads over an
online social network like Twitter. The LIWC analysis points to a remarkable
observation these late tweets are essentially laden in words from opinion and
realize categories which indicates that, given sufficient time, the wisdom of
the crowd is actually able to tell apart the exaggerated news. As a second step
we study the characteristics of the users who never or rarely post exaggerated
news content and compare them with those who post exaggerated news content more
frequently. We observe that the latter class of users have less retweets or
mentions per tweet, have significantly more number of followers, use more slang
words, less hyperbolic words and less word contractions. We also observe that
the LIWC categories like bio, health, body and negative emotion are more
pronounced in the tweets posted by the users in the latter class. As a final
step we use these observations as features and automatically classify the two
groups achieving an F1 score of 0.83.
