ABSTRACT
Viral diseases are the most devastating health concern worldwide. Outbreaks of coronavirus (CoVs)-related acute respiratory diseases are responsible for the massive health/socio-economic breakdown in the last two decades including the Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), the third reported spillover SARS-CoV-2 from an animal coronavirus to humans. After the H1N1 pandemic influenza (2009), SARS-CoV-2 (novel-beta coronavirus) causing COVID-19 has stretched across 215 countries in 5 major continents with 200,523,190 confirmed cases (4 August 2021;https://www.worldometers.info/coronavirus/). COVID-19 patients had cough, fever, dyspnea, headache, and respiratory failure, as well as shock, acute respiratory distress syndrome, and sepsis in severe instances. Independent of two preceding epidemics, SARS (2002) and MERS (2012), a knowledge gap about the emerging medical manifestations as well as complications of SARS-CoV-2 (2019-2020) infections in humans must be filled, with a focus on immunological complications and computational genomics for forecasting/preparedness for a similar outbreak in the future. This paper aims to address aspects of this gap.
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