Misinformation, Disinformation and Mal-information: A discussion on the role of fake news on the COVID-19 outbreak

The fact that we are living at exceptional times is undeniable. The coronavirus pandemic situation is a crisis of unprecedented consequences that will change our lives forever. In the face of popularisation of social media, the communication between people has become faster and easier. These tools could be used to make our isolation easier, however, many times these networks are used to publicise information that is not correct and have the simple goal of misinforming, being it for personal gain, lack of knowledge or malicious behaviour towards the other, causing issues related with failing of social isolation, for instance. There is still no vaccine to contain this pandemic, which has already produced around 247K deaths worldwide and 28,446 in the UK until 04/05/2020. In a context of uncertainty in which health care professionals and policy/decision makers from around the planet are running out of time to find a way to neutralize this disease, social isolation has been considered the most efficient measure to prevent the collapse of the world's health systems. However, there has been an increase in the acceptance by the general public to comply with it exactly because of fake news. 

Covid-19 is a new disease and information on risk factors for critical health developments is limited. Based on the information currently available and clinical knowledge, the elderly and people of any age with underlying medical conditions may be at greater risk of medical complications because of Covid-19. And so far, the underlying medical conditions that have been associated with a higher risk of complications is anyone with a medical condition (morbidity) associated, such as: arterial hypertension, diabetes mellitus, cardiovascular diseases, chronic respiratory diseases and cancer. And this list is being revised and updated almost daily with the evolution of the disease.

However, this information becomes irrelevant when you have governments that try to follow a “herd immunity” strategy or call COVID-19 as “only a mild flu”. In some countries, such as the USA, Brazil, the UK, and Sweden, there was still an attempt to undermine the severity of the illness and that gave a lot of fuel to the dissemination of fake news related to COVID-19. 

Thus, you have people sharing fake news messages to target such specific situations. 

The dissemination of fake news is a problem that has already been addressed but by no means is solved. After the manipulation of Cambridge Analytica on the Brexit campaign, the Trump election and the Bolsonaro election, it is very clear the manipulation of information can have a real impact on society in ‘normal times’. During a pandemic, fake news can be the difference between life and death when the data shared can directly hurt the people who are believing in it.  

The impact of fake news regarding the disease treatment, public sector actions, social isolation and quarantine imposition and the number of cases registered is very hard to calculate.

There are three types of ‘fake news’ that can be described in the literature:

  • Misinformation (FN): Information that is false, but not created with the intention of causing harm (e.g. someone posting an article containing now out of date information but not realizing it).
  • Disinformation (FN): Information that is false and deliberately created to harm a person, social group, organization or country (e.g. a competitor purposely posting false statistics about your organization with an intent to discredit you)
  • Mal-information (FN): Information that is based on reality, used to inflict harm on a person, organization or country (e.g. someone using a picture of a dead child refugee (with no context) in an effort to ignite hatred of a particular ethnic group they are against.

The types of the misinformation can be directed to most diverse aspects including new miraculous medication, diet that will protect you from the virus, and the worst of all, the question if the virus really exists with the intent of misleading society.

Identifying who wrote such text is not easy and there are several possible ways of doing it, such as using natural language processing or machine learning algorithms that can investigate and perform predictions using the meta-data associated with it.

Such tools can be used by Health Systems or other public organisations to adopt machine learning related techniques in order to create an automated way to promote greater security and reliability to the identification of misinformation related with COVID-19 in social networks. 

This can have a direct impact on the effectiveness of how the governments can promote and get the importance of the social isolation measures, for example, as well as any other  guidance that has to be fed correctly related with new cases, deaths and the situation of the curve for the health system.

This will include, but be not limited by:

  • Collecting and classifying a new misinformation dataset from Twitter data that will include fake news and real correct information. This will be done by using the expertise of specialists. 
  • The proposition of a systematic evaluation of how to identify fake news related to COVID-19, as well as the features or meta-data associated with the most common fake news in the dataset.
  • Analyse which of the existing machine learning models can work better with such type of meta-data, avoiding the need to perform social media sentiment analytics by only using Natural Language Processing (NPL).
  • Congregate the information about death and confirmed cases in both specific places once these numbers are consolidated considering cases registered in hospitals as well as in the community in the same period of the Twitter data.

The protection of cities and their population is a most important requirement for national and regional governments, and for the well-being of citizens. 

Advances in this type of forensic sciences can provide effective crime detection and prevention techniques which can be easily accessed in the so-called "first world’ countries, but this is not generally the case for countries which are still in development. So, we must continue to investigate which techniques can be used for a fast and effective detection of fake news disseminated in social media and better understand its behaviour so we can prevent its impact in extreme adverse situations such as the coronavirus crisis we are living at the moment.

Dr. Marjory Da Costa-Abreu

Sheffield Hallam University/Federal University of Rio Grande do Norte (UFRN)





Case Study