Using Machine Learning to Distinguish Between What’s Real and What Is Not

Using Machine Learning to Distinguish Between What’s Real and What Is Not

A neural network approach towards fake news detection in a surreal world. I will try to use a Machine Learning (ML) technique to classify headlines as fake or not and compare it to humans.

This article came as an idea after 2020 was getting increasingly surreal. The world is becoming stranger and we are receiving more info from sources all over the internet, some of them being dishonest at best. The ability to know if what you read is fake or genuinely bizarre and shocking is getting harder and harder. And with USA elections being less than a month away, it is important to be able to stand out reality from fake news.

I will try to use a Machine Learning (ML) technique to classify headlines as fake or not and compare it to humans.

Human Approach

I think we should start with an experiment. I would use two titles the one is from a fake article (from The Onion) and the other one is from a real article.

The answer isn’t obvious and sadly that’s not the only case. You can find the answer at the end of the article but don’t spoil yourself.

I decided to do one more little experiment. I chose six headlines (three fake and three real). Then I went to Instagram and I did a poll between my followers. I know, the sample is more than biased by not being representative for nothing but my friends, nonetheless it is going to be an indicator.

Collective human knowledge had a success rate (accuracy) of 0.5. Practically not being any better than tossing a coin and relying on pure luck.

Let’s see how the “machines” are going to perform.

neural-networks nlp fake-news machine-learning data-science

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