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Due to extensive spread of fake news on social and news media it became anemerging research topic now a days that gained attention. In the news media andsocial media the information is spread highspeed but without accuracy and hencedetection mechanism should be able to predict news fast enough to tackle thedissemination of fake news. It has the potential for negative impacts onindividuals and society. Therefore, detecting fake news on social media isimportant and also a technically challenging problem these days. We knew thatMachine learning is helpful for building Artificial intelligence systems basedon tacit knowledge because it can help us to solve complex problems due to realword data. On the other side we knew that Knowledge engineering is helpful forrepresenting experts knowledge which people aware of that knowledge. Due tothis we proposed that integration of Machine learning and knowledge engineeringcan be helpful in detection of fake news. In this paper we present what is fakenews, importance of fake news, overall impact of fake news on different areas,different ways to detect fake news on social media, existing detectionsalgorithms that can help us to overcome the issue, similar application areasand at the end we proposed combination of data driven and engineered knowledgeto combat fake news. We studied and compared three different modules textclassifiers, stance detection applications and fact checking existingtechniques that can help to detect fake news. Furthermore, we investigated theimpact of fake news on society. Experimental evaluation of publically availabledatasets and our proposed fake news detection combination can serve better indetection of fake news.

Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey