Te Ahorré Un Click: A Revised Definition of Clickbait and Detection in Spanish News

• URL: http://arxiv.org/abs/2507.09777v1
• Date: Sun, 13 Jul 2025 20:19:08 GMT
• Title: Te Ahorré Un Click: A Revised Definition of Clickbait and Detection in Spanish News
• Abstract summary: We revise the definition of clickbait, which lacks current consensus.<n>We release TA1C, the first open source dataset for clickbait detection in Spanish.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• An Interpretable Benchmark for Clickbait Detection and Tactic Attribution [37.86790434630698]
  The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media.<n>This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies.<n>We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution.
  arXiv  Detail & Related papers  (2025-09-13T18:26:49Z)
• What Makes You CLIC: Detection of Croatian Clickbait Headlines [0.0]
  This paper is a novel dataset for clickbait detection of Croatian news headlines spanning a 20-year period and encompassing mainstream and fringe outlets.<n>We fine-tune the BERTi'c model on this task and compare its performance to LLM-based ICL methods with prompts in Croatian and English.<n>We find that nearly half of the analyzed headlines contain clickbait, and that finetuned models deliver better results than general LLMs.
  arXiv  Detail & Related papers  (2025-07-18T18:39:07Z)
• Prompt-tuning for Clickbait Detection via Text Summarization [18.027598728494485]
  Clickbaits are surprising social posts or deceptive news headlines that attempt to lure users for more clicks. Most existing methods compute the semantic similarity between the headlines and contents for detecting clickbait. We propose a prompt-tuning method for clickbait detection via text summarization.
  arXiv  Detail & Related papers  (2024-04-17T09:39:02Z)
• BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset [0.6008132390640294]
  We have constructed the first Bangla clickbait detection dataset containing 15,056 labeled news articles and 65,406 unlabelled news articles. Each article has been labeled by three expert linguists and includes an article's title, body, and other metadata. We finetune a pretrained Bangla transformer model in an adversarial fashion using Semi Supervised Generative Adrial Networks (SS GANs)
  arXiv  Detail & Related papers  (2023-11-10T17:38:46Z)
• Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks [60.14025705964573]
  SheepDog is a style-robust fake news detector that prioritizes content over style in determining news veracity. SheepDog achieves this resilience through (1) LLM-empowered news reframings that inject style diversity into the training process by customizing articles to match different styles; (2) a style-agnostic training scheme that ensures consistent veracity predictions across style-diverse reframings; and (3) content-focused attributions that distill content-centric guidelines from LLMs for debunking fake news.
  arXiv  Detail & Related papers  (2023-10-16T21:05:12Z)
• A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles [29.119911024232064]
  There is no publicly available clickbait corpus for the Romanian language. We introduce a novel Romanian Clickbait Corpus (RoCliCo) comprising 8,313 news samples which are manually annotated with clickbait and non-clickbait labels. We propose a novel BERT-based contrastive learning model that learns to encode news titles and contents into a deep metric space.
  arXiv  Detail & Related papers  (2023-10-10T11:38:16Z)
• An Offline Metric for the Debiasedness of Click Models [52.25681483524383]
  Click models are a common method for extracting information from user clicks.<n>Recent work shows that the current evaluation practices in the community fail to guarantee that a well-performing click model generalizes well to downstream tasks.<n>We introduce the concept of debiasedness in click modeling and derive a metric for measuring it.
  arXiv  Detail & Related papers  (2023-04-19T10:59:34Z)
• Verifying the Robustness of Automatic Credibility Assessment [50.55687778699995]
  We show that meaning-preserving changes in input text can mislead the models. We also introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks. Our experimental results show that modern large language models are often more vulnerable to attacks than previous, smaller solutions.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection [118.36746273425354]
  This paper presents a paralleled visual-concept pre-training method for open-world detection by resorting to knowledge enrichment from a designed concept dictionary. By enriching the concepts with their descriptions, we explicitly build the relationships among various concepts to facilitate the open-domain learning. The proposed framework demonstrates strong zero-shot detection performances, e.g., on the LVIS dataset, our DetCLIP-T outperforms GLIP-T by 9.9% mAP and obtains a 13.5% improvement on rare categories.
  arXiv  Detail & Related papers  (2022-09-20T02:01:01Z)
• WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations [64.52412111417019]
  We propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick. Since detailed semantic information is not captured by clicks, directly training with click labels leads to poor segmentation predictions. WeClick outperforms the state-of-the-art methods, increases performance by 10.24% mIoU than baseline, and achieves real-time execution.
  arXiv  Detail & Related papers  (2021-07-07T09:12:46Z)
• Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) [0.0]
  A neural network with a pre-trained language model M-BERT that acted as a embedding layer is combined with a 100 nodes hidden layer to detect clickbait headlines. With a total of 6632 headlines as a training dataset, the classifier performed remarkably well. The usage of multilingual BERT in Indonesian text classification task was tested and is possible to be enhanced further.
  arXiv  Detail & Related papers  (2021-02-02T14:13:02Z)
• Hooks in the Headline: Learning to Generate Headlines with Controlled Styles [69.30101340243375]
  We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options. TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.
  arXiv  Detail & Related papers  (2020-04-04T17:24:47Z)
• Clickbait Detection using Multiple Categorization Techniques [6.396288020763144]
  This paper proposes a hybrid categorization technique for separating clickbait and non-clickbait articles. The proposed hybrid model is more robust, reliable and efficient than any individual categorization techniques.
  arXiv  Detail & Related papers  (2020-03-29T07:16:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.