Just like a bait that is used to lure fishes, clickbait is used to lure humans towards content which is generally peppered with advertisements. One of the biggest disappointments that many of us face after clicking on such an article is that it rarely contains any actual, useful information which is true to the catchy headline it carries.

Many wonder what is the driving force behind the rise of clickbait: it is basically the ever-increasing competition that websites are having with each other. In order to get more “views” or “clicks” on their content, these websites make use of clickbait.

Clickbait has been around for almost a decade now, with catchy titles, attention-grabbing descriptions and eye-catching photographs popping up everywhere. Even though it was once an internet marketing strategy, today, clickbaits have become a big issue —marketers are using clickbait that has no relationship with core content.

In order to mitigate clickbait, Facebook took an initiative back in 2014. In 2016 and 2017, the social media giant released more updates on the same.  That is not all, in 2018, Mark Zuckerberg wrote in a blog post details of the ways Facebook was dealing with problematic content going forward.

Machine Learning Mitigating Click baits

We all know that machine learning has the ability to tackle some of the critical issues on the internet and the tech world. With time, machine learning technologies have evolved to such an extent that today it is even used to detect clickbait.

There are many projects and paper available on the internet that has shown how to use machine learning and spot click bait. In this article, we will have a look at one of the ways of detecting clickbait.

“Stop Clickbait: Detecting and Preventing Click bait in Online News Media” is one of the studies focused on mitigating clickbait. This entire process of mitigating clickbait consists of a browser extension that uses machine learning and natural language processing to identify headlines automatically and then warns the readers of different media sites about the chances of being baited by epic headlines. That is not all, the machine learning based-browser extension also gives its readers the option to block click baits s/he doesn’t want to see.

To tell you more about how ‘Stop Clickbait’ works, the chrome extension always looks for the Document Object Model (DOM) when a website is loaded. It then scans the DOM and tries to find if there is an anchor element (a href = ….) and anchor tag. Also, it looks if it has an anchor text.

So, if it discovers anchor text, it sends it to a server, where the text goes through the clickbait classifier. And if it fails to find anchor text or there is no anchor text available, the extension then sends the URL to the server where it makes a GET request for the webpage title and runs the classifier on the obtained title.

This is how ‘Stop Clickbait’ works. This clickbait detection extension marks the clickbaits as green and leaves unmarked when it is not a click bait.

Bottom Line

Creating top-notch content and engaging with the audience is something every content creator wants to do. However, there are people who take a different path and use click baits to get engagement. Clickbaits are not something completely wrong but they should have some relation with the core content. Clickbaits today are far from being related to the content, rather, they are more of something that misleads the audience. And in order to stop clickbaits, it is high time that sought after technologies like machine learning and artificial intelligence are used more.

Word to the wise: If you are a content creator, it is always considered to be a good practice to use tiles/headlines/thumbnails that has some relation to your content.