Want to detect fake news with natural language processing? Here are the top ways

In the wake of growing fake news, it becomes a difficult task to distinguish between the fake and the real ones, however, growth in natural language processing (NLP) offers some kind of a possible solution. 

People can now access news from a wide variety of sources thanks to the dissemination of information through social media and internet platforms in the current digital era. The downside of this independence is the rise of fake news. Inaccurate material that has been deliberately shared to mislead the public and erode trust in professional media is referred to as fake news. 

Identifying and eradicating fake news to keep the community united and informed is a must-do thing. But how? 

What is NLP?

A significant tool for spotting false information is natural language processing (NLP), a branch of artificial intelligence that offers computers the ability to understand and interpret human language. This article explores how NLP can be applied to uncover false information and provides instances of its use. 

Let’s look at some techniques to detect fake news. 

Sentimental analysis 

NLP-based sentiment analysis can be a useful technique for spotting fake news. By examining the emotions expressed in a news article or social media post, natural language processing (NLP) algorithms can determine the author's aim and any biases. The use of obscene language or exaggeration in fake news typically preys on readers' emotions.
An NLP-based sentiment analysis algorithm, for example, can recognise a news item covering a political occurrence as being considerably biased in favour of a particular party and employing emotionally charged language to impact public opinion. 

Semantic analysis

Fact-checking technologies powered by NLP can examine the content of a news article against dependable sources or databases to certify the accuracy of the information. The semantic analysis aids in comprehending the meaning and context of the language that is being used by pointing out contradictions and inconsistencies that may indicate fake news.
For instance, an NLP-based fact-checking system can rapidly verify the veracity of a news article's claim that a well-known celebrity recommends a contested product by comparing it to credible sources.

Named entity recognition (NER)

Named entity recognition (NER), a technique used in NLP enables computers to identify and classify specific entities referenced in a text, such as people, groups, places, or dates. Fake news can be exposed by locating inconsistencies or made-up information by identifying key participants.

Mentions in news stories about supposed environmental disasters are examples of nonexistent organisations or locations that NER algorithms may identify as potential indicators of false news.

Analysing sensationalism and clickbait

NLP models can be trained to recognise clickbait headlines and overly dramatic language, both of which are signs of fake news. These techniques can help to weed out fake information and prioritise reliable news sources.

For instance, by analysing headlines and content using an NLP-powered algorithm, sensational phrases and exaggerated claims that usually go with clickbait stories can be detected.

Checking the legitimacy of the source 

The past performance of news organisations, including their reputation, dependability, and historical reporting accuracy, can be analysed using NLP techniques. This information can be used to assess the reliability of recent content and identify possible fake news sources.
For instance, before determining the information to be trustworthy, an NLP-powered system may assess the reliability of a less well-known website that posted a surprising news article.