ABSTRACT
Journalism has always remained a vital constituent of our society and journalists play a key role in making people aware of the happenings and developments in society. This spread of information enables shaping the ideologies, orientations and thoughts of individuals as well as the society. Contrary to this, the spread of misinformation or fake news leads to detrimental consequences. With the advent of social media, the menace of fake news has become grievous due to the unrestrained propagation of information and difficulty to track several accounts operated by humans or bots. This menace can be mitigated through data science approaches by combining artificial intelligence with statistics and domain-based knowledge. In this paper, a survey of works aimed at characterization, feature extraction and subsequent detection of fake news has been conducted from a data science perspective. Along with it, an analysis of the 8 renowned fake news detection repositories has been presented. Furthermore, through a case study on tweets related to COVID-19 pandemic, the factors behind the spread of misinformation during critical times, distinguishing between factual and emotional tweets and viable approaches to restrain fake news has been enunciated. © 2021 The Authors