Te Ahorr\'e Un Click: A Revised Definition of Clickbait and Detection in Spanish News

TL;DR

This paper redefines clickbait focusing on the curiosity gap, introduces a new detection dataset in Spanish, and provides baseline models with high accuracy for identifying clickbait headlines.

Contribution

It offers a revised, clearer definition of clickbait, and presents TA1C, the first annotated Spanish clickbait dataset, along with strong baseline detection methods.

Findings

TA1C dataset contains 3,500 annotated tweets

Achieved 0.84 F1-score with baseline models

High inter-annotator agreement of 0.825 Fleiss' K

Abstract

We revise the definition of clickbait, which lacks current consensus, and argue that the creation of a curiosity gap is the key concept that distinguishes clickbait from other related phenomena such as sensationalism and headlines that do not deliver what they promise or diverge from the article. Therefore, we propose a new definition: clickbait is a technique for generating headlines and teasers that deliberately omit part of the information with the goal of raising the readers' curiosity, capturing their attention and enticing them to click. We introduce a new approach to clickbait detection datasets creation, by refining the concept limits and annotations criteria, minimizing the subjectivity in the decision as much as possible. Following it, we created and release TA1C (for Te Ahorr\'e Un Click, Spanish for Saved You A Click), the first open source dataset for clickbait detection in Spanish. It consists of 3,500 tweets coming from 18 well known media sources, manually annotated and reaching a 0.825 Fleiss' K inter annotator agreement. We implement strong baselines that achieve 0.84 in F1-score.