mlsanityData to 14 Oct 2026

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

arXiv 2507.09777 ↗First posted 13 Jul 2025cs.CL cs.SIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 13 Jul 2025.

Builds on

10 of 35 references.

References · 10

  1. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  2. 2023Spanish Pre-trained BERT Model and Evaluation Datacited by 81
  3. 2016Stop Clickbait: Detecting and Preventing Clickbaits in Online News Mediacited by 39
  4. 2017Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?cited by 11
  5. 2017Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twittercited by 5
  6. 2017Clickbait Detection in Tweets Using Self-attentive Networkcited by 5
  7. 2018Using Neural Network for Identifying Clickbaits in Online News Mediacited by 5
  8. 2023A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articlescited by 3
  9. 2017Machine Learning Based Detection of Clickbait Posts in Social Mediacited by 2
  10. 2018How Curiosity can be modeled for a Clickbait Detectorcited by 1

Built on by

Not cited yet.