# Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media

arXiv: https://arxiv.org/abs/1610.09786 · HTML: https://mlsanity.com/paper/1610.09786/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 30 of 39)

- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack](https://mlsanity.com/paper/2407.16695/) (2024)
- [Clickbait Spoiling via Question Answering and Passage Retrieval](https://mlsanity.com/paper/2203.10282/) (2022)
- [Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators](https://mlsanity.com/paper/2411.05854/) (2024)
- [How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?](https://mlsanity.com/paper/2107.07970/) (2021)
- [APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT](https://mlsanity.com/paper/2402.01697/) (2024)
- [Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.05133/) (2022)
- [Clickbait Detection via Large Language Models](https://mlsanity.com/paper/2306.09597/) (2023)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals](https://mlsanity.com/paper/2305.11349/) (2023)
- [Logistic Regression makes small LLMs strong and explainable "tens-of-shot" classifiers](https://mlsanity.com/paper/2408.03414/) (2024)
- [Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning](https://mlsanity.com/paper/2505.17448/) (2025)
- [Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)](https://mlsanity.com/paper/2102.01497/) (2021)
- [Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2602.18171/) (2026)
- [Russian News Clustering and Headline Selection Shared Task](https://mlsanity.com/paper/2105.00981/) (2021)
- [Incongruity Detection between Bangla News Headline and Body Content through Graph Neural Network](https://mlsanity.com/paper/2211.07709/) (2022)
- [BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset](https://mlsanity.com/paper/2311.06204/) (2023)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)
- [LLM-guided headline rewriting for clickability enhancement without clickbait](https://mlsanity.com/paper/2603.22459/) (2026)
- [Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT](https://mlsanity.com/paper/2102.10601/) (2021)
- [Is it a click bait? Let's predict using Machine Learning](https://mlsanity.com/paper/2106.07348/) (2021)
- [Clickbait Detection in YouTube Videos](https://mlsanity.com/paper/2107.12791/) (2021)
- [POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence](https://mlsanity.com/paper/2111.03547/) (2021)
- [Islander: A Real-Time News Monitoring and Analysis System](https://mlsanity.com/paper/2204.11457/) (2022)
- [How learners produce data from text in classifying clickbait](https://mlsanity.com/paper/2302.01292/) (2023)
- [Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning](https://mlsanity.com/paper/2405.04292/) (2024)
- [Generating clickbait spoilers with an ensemble of large language models](https://mlsanity.com/paper/2405.16284/) (2024)

## Abstract (arXiv)

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.