  Withtheadventofsocialmedia,therehasbeenanextremely rapid increase in the
content shared online. Consequently, the propagation of fake news and hostile
messages on social media platforms has also skyrocketed. In this paper, we
address the problem of detecting hostile and fake content in the Devanagari
(Hindi) script as a multi-class, multi-label problem. Using NLP techniques, we
build a model that makes use of an abusive language detector coupled with
features extracted via Hindi BERT and Hindi FastText models and metadata. Our
model achieves a 0.97 F1 score on coarse grain evaluation on Hostility
detection task. Additionally, we built models to identify fake news related to
Covid-19 in English tweets. We leverage entity information extracted from the
tweets along with textual representations learned from word embeddings and
achieve a 0.93 F1 score on the English fake news detection task.
