Fake News Detection in Machine Learning [Explained with Coding Example]

Fake News Detection in Machine Learning [Explained with Coding Example]

Machine Learning techniques using Natural Language Processing and Deep Learning can be used to tackle this problem to some extent. We will be building a Fake News Detection model using Machine Learning in this tutorial.

Fake news is one of the biggest issues in the current era of the internet and social media. While it’s a blessing that the news flows from one corner of the world to another in a matter of a few hours, it is also painful to see many people and groups spreading fake news.

Machine Learning techniques using Natural Language Processing and Deep Learning can be used to tackle this problem to some extent. We will be building a Fake News Detection model using Machine Learning in this tutorial.

By the end of this article, you will know the following:

  • Handling text data
  • NLP processing techniques
  • Count vectorization & TF-IDF
  • Making predictions and classifying news text

Data & Problem

We will be using the Kaggle Fake News challenge data to make a classifier. The dataset consists of 4 features and 1 binary target. The 4 features are as follows:

  1. id: unique id for a news article
  2. title: the title of a news article
  3. author: author of the news article
  4. text: the text of the article; could be incomplete

And the target is “label” which contains binary values 0s and 1s. Where 0 means it is a reliable source of news, or in other words, Not Fake. 1 means that it is a piece of potentially fake news and not reliable. The dataset we have consisted of 20800 instances. Let’s dive right in.

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