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ConceptTheoretical Framework1.2.5

Clickbait

It is a deceptive headline technique aiming to ensure the content is clicked by intentionally provoking the reader’s curiosity with incomplete information.

Clickbait looks for the value of the news not in its content, but in the click-through rate of the headline. Instead of giving a summary of the event, an information gap is created with expressions like ‘Here is that detail’, ‘It shocked those who saw it’. The reader is forced to click the link to complete the missing information. (Note: Clickbait is the name of a concept. MagPunk’s ‘low quality’ label does not map onto it one to one: it catches measurable patterns and often misses headlines that merely leave a curiosity gap — see 2.3.3.)

Example: Giving the result of a match as ‘Surprise score in the giant derby!’ and explaining the score only in the last paragraph of the news.

Go deeper

This method is a direct result of the digital advertising model. Systems that generate revenue per page view focus on drawing the user to the site at all costs. Clickbait not only steals the reader’s time, but also devalues the content and context of the news. Although it provides a short-term interaction increase, it damages the relationship of trust between the reader and the publisher in the long run. Quality journalism is based on the principle of giving the most critical information directly in the headline instead of creating an information gap.

Up next2.3.3 Low Quality Label

1.2.5 M2: Clickbait Test

M2

Examine six headlines, decide if they are clickbait, and compare with the model's verdict.

What does it mean to dream about water?
How to save money on your next holiday
Ten golden tips for anyone who wants to lose weight
You won't believe what happened next in the city centre
The detail everyone missed in yesterday footage
Two streets closed to traffic for metro construction
The headlines in this module were written as examples; they do not belong to any publisher. The verdicts and word contributions are the real output of the classifier published on the Transparency page, run on these headlines — you can download the same model and repeat it. The punctuation attached to some words is how the model sees the text.