  COVID-19 impacted every part of the world, although the misinformation about
the outbreak traveled faster than the virus. Misinformation spread through
online social networks (OSN) often misled people from following correct medical
practices. In particular, OSN bots have been a primary source of disseminating
false information and initiating cyber propaganda. Existing work neglects the
presence of bots that act as a catalyst in the spread and focuses on fake news
detection in 'articles shared in posts' rather than the post (textual) content.
Most work on misinformation detection uses manually labeled datasets that are
hard to scale for building their predictive models. In this research, we
overcome this challenge of data scarcity by proposing an automated approach for
labeling data using verified fact-checked statements on a Twitter dataset. In
addition, we combine textual features with user-level features (such as
followers count and friends count) and tweet-level features (such as number of
mentions, hashtags and urls in a tweet) to act as additional indicators to
detect misinformation. Moreover, we analyzed the presence of bots in tweets and
show that bots change their behavior over time and are most active during the
misinformation campaign. We collected 10.22 Million COVID-19 related tweets and
used our annotation model to build an extensive and original ground truth
dataset for classification purposes. We utilize various machine learning models
to accurately detect misinformation and our best classification model achieves
precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot
analysis indicates that bots generated approximately 10% of misinformation
tweets. Our methodology results in substantial exposure of false information,
thus improving the trustworthiness of information disseminated through social
media platforms.
