Te Ahorré Un Click: A Revised Definition of Clickbait and Detection in Spanish News
This work addresses the problem of inconsistent clickbait definitions and detection for Spanish news media, offering a new dataset and baseline models.
The authors tackled the lack of consensus in defining clickbait by proposing a new definition based on creating a curiosity gap, and they released TA1C, the first open-source Spanish clickbait detection dataset with 3,500 tweets and strong baselines achieving 0.84 F1-score.
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 Ahorré 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.