An Interpretable Benchmark for Clickbait Detection and Tactic Attribution

• URL: http://arxiv.org/abs/2509.10937v1
• Date: Sat, 13 Sep 2025 18:26:49 GMT
• Title: An Interpretable Benchmark for Clickbait Detection and Tactic Attribution
• Abstract summary: 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.
• Score: 37.86790434630698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack of explainability limits their practical adoption. This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies. We introduce a synthetic dataset generated by systematically augmenting real news headlines using a predefined catalogue of clickbait strategies. This dataset enables controlled experimentation and detailed analysis of model behaviour. We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution. In the first stage, we compare a fine-tuned BERT classifier with large language models (LLMs), specifically GPT-4.0 and Gemini 2.4 Flash, under both zero-shot prompting and few-shot prompting enriched with illustrative clickbait headlines and their associated persuasive tactics. In the second stage, a dedicated BERT-based classifier predicts the specific clickbait strategies present in each headline. This work advances the development of transparent and trustworthy AI systems for combating manipulative media content. We share the dataset with the research community at https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection

Related papers

• ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models [3.131740922192115]
  This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles.<n>The application employs a hybrid machine learning architecture that combines transformer-based embeddings with linguistically motivated features and a custom "baitness" score.<n>The tool can warn users before and after they access a clickbait article.
  arXiv  Detail & Related papers  (2026-05-18T10:12:47Z)
• 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)
• Te Ahorré Un Click: A Revised Definition of Clickbait and Detection in Spanish News [0.0]
  We revise the definition of clickbait, which lacks current consensus.<n>We release TA1C, the first open source dataset for clickbait detection in Spanish.
  arXiv  Detail & Related papers  (2025-07-13T20:19:08Z)
• 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)
• Learnability Gaps of Strategic Classification [68.726857356532]
  We focus on addressing a fundamental question: the learnability gaps between strategic classification and standard learning. We provide nearly tight sample complexity and regret bounds, offering significant improvements over prior results. Notably, our algorithm in this setting is of independent interest and can be applied to other problems such as multi-label learning.
  arXiv  Detail & Related papers  (2024-02-29T16:09:19Z)
• 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)
• 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)
• Revisiting Self-Training for Few-Shot Learning of Language Model [61.173976954360334]
  Unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.
  arXiv  Detail & Related papers  (2021-10-04T08:51:36Z)
• 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)
• Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data [61.789797281676606]
  We propose a novel meta-learning latent variable approach, called MetaBridge. It can learn transferable knowledge from a subset of categories with limited labeled data. It can capture the uncertainty of never-seen categories with unlabeled data.
  arXiv  Detail & Related papers  (2020-06-15T21:31:05Z)
• 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.