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Misinformation – the difference between life and death

Misinformation can mean the difference between life and death

We are very lucky to live in the age of the internet, with all the knowledge available at the tip of our fingers. Nowadays, we don’t have to struggle to find something about a topic, but we do face a different challenge in finding the correct information.

 As anybody has an ability to create a post and share information, the possibility of misinformation has drastically increased. In most extreme cases, the misinformation can be the difference between life and death.


This was also recently showcased with the updates and advice on the Coronavirus outbreak. It turned out the bad advice on social media spread quicker than the virus.

 As Professor Hunter (University of East Anglia’s Norwich Medical School),  who had already run tests on the effect of misinformation on the disease outbreaks, recently found out, it is more likely people on social media share wrong information instead of good advice provided by NHS or the World Health Organization.

This includes the origin of the virus, the symptoms and how it’s spread. That means people suffer because they take the wrong precautions due to bad advice.

Just recently a woman from London, diagnosed with Coronavirus, arrived at the hospital with an Uber and went straight to the reception desk. Two hospital staff members had to impose self-isolation, and more people were at risk of infection. All of this could be prevented with the right information.

How to tackle the fake news?

The recent example is just one of many where the spread of misinformation causes all sorts of havoc. The fake news is often interesting and intriguing, so it’s more likely to be shared than the real news. So, are there any possible solutions?

As this is a widespread issue that requires a quick response, there has been a recent consensus on how to approach this issue: combining an automated machine learning system with the general human knowledge – on a large scale.

The beauty of this combination is as follows: as machine learning recognizes patterns and improves based on past data and results, it can be evolved into a powerful tool when dealing with large amounts of information. The automated systems are also the best answer to the everpresent urgency of the situation. But here we come to the other part of the hybrid system – the human input.


To be clear, people vary greatly in terms of knowledge, expertise, interests, experience, and behavior. However, the Machine Learning models have the capability to take all these differences into account to create the earliest, robust rules. Technically, this means that the models can calculate different weightings for each user based on their previous accuracy, usage behaviors, and connections.


Blockbird is a startup that is actively solving the issue by using the explained combination of machine learning algorithms and human input.

This is done by gathering the information, evaluating it and further present it in a user-friendly way. With text analytics, the raw information is converted into meaningful data, used as an input to the Machine Learning algorithms.

Furthermore, the models calculate different weightings for each user based on their news ratings, usage behaviors, and connections while eliminating unnecessary information.

Evaluation, rating, and rewards

Why would people participate as evaluators of the news?  First of all, they are self-incentivized to cooperate as everyone benefits from accurate real-time information. However, for further encouragement there is a reward system in place, awarding the most helpful people with cryptocurrency.

The most helpful evaluators are determined by the rating system that scores and ranks users based on their contribution. This was made possible only recently with the blockchian technology that enables a transparent decentralized system that acts in real-time.

Why is this such a big deal? There hasn’t been anyone yet who has approached news evaluation in such a complete manner. By using the three pillars: the rating, evaluation, and reward system. All three are interconnected in a way that enables to provide the best possible news evaluation. With a solid foundation for news evaluation, the machine learning algorithms can really shine.


Recent Team Developments

The project team consists of professionals in a variety of fields. Experts on data analysis, blockchain, and business and economics are a part of the team from the start, however, recently the team grew for an in-house designer and a journalist to really be present in the news arena. With such a large team the project really seems to be covering all aspects of their future endeavors.

Investment opportunity

As the project is still in an early phase of development, it presents an interesting opportunity for investment. Tackling the problem of misinformation and news handling is a significant one. There is definitely a big market for a complete solution. Should they succeed in presenting it, we could be hearing a lot more about it.

If you want to take a deeper look into the project and their initial exchange offer, you can visit their webpage. With a complete approach to one of the biggest persisting problems, the world faces today the project certainly doesn’t lack ambition. And as with all ambitious projects, the possible risks and rewards are certainly there.

March 25, 2020, Klemen Methans