  Due to extensive spread of fake news on social and news media it became an
emerging research topic now a days that gained attention. In the news media and
social media the information is spread highspeed but without accuracy and hence
detection mechanism should be able to predict news fast enough to tackle the
dissemination of fake news. It has the potential for negative impacts on
individuals and society. Therefore, detecting fake news on social media is
important and also a technically challenging problem these days. We knew that
Machine learning is helpful for building Artificial intelligence systems based
on tacit knowledge because it can help us to solve complex problems due to real
word data. On the other side we knew that Knowledge engineering is helpful for
representing experts knowledge which people aware of that knowledge. Due to
this we proposed that integration of Machine learning and knowledge engineering
can be helpful in detection of fake news. In this paper we present what is fake
news, importance of fake news, overall impact of fake news on different areas,
different ways to detect fake news on social media, existing detections
algorithms that can help us to overcome the issue, similar application areas
and at the end we proposed combination of data driven and engineered knowledge
to combat fake news. We studied and compared three different modules text
classifiers, stance detection applications and fact checking existing
techniques that can help to detect fake news. Furthermore, we investigated the
impact of fake news on society. Experimental evaluation of publically available
datasets and our proposed fake news detection combination can serve better in
detection of fake news.
