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. We share the dataset with the research community at https://github.com/LLM - HITCS25S/ClickbaitTacticsDetection The widespread use of clickbait headlines in digital media has become a pervasive challenge, undermining the credibility of information and exploiting user attention through manipulative linguistic techniques. While automated systems for detecting clickbait have improved in recent years, their focus has remained mainly on binary classification, simply labelling content as clickbait or not. However, effective mitigation of such content requires going beyond detection to understanding how and why certain headlines manipulate readers. Specifically, it is crucial to evaluate whether current AI models can accurately recognize and distinguish the diverse linguistic styles and persuasive strategies commonly employed in clickbait.

We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans. This task demands advanced text understanding and summarization abilities, challenging the models' capacity to infer and connect diverse pieces of information to meet the user's informational needs generated by the clickbait headline. We evaluate the Spanish text comprehension capabilities of a wide range of state-of-the-art large language models. Additionally, we use the dataset to train ClickbaitFighter, a task-specific model that achieves near-human performance in this task.

Clickbait might not lure readers as before – and using artificial intelligence to detect fake news might be much more complex than previously thought, a team of researchers suggest. Clickbait headlines might not be as enticing to readers as once thought, according to a team of researchers. They added that artificial intelligence – AI – may also come up short when it comes to correctly determining whether a headline is clickbait. In a series of studies, the researchers found that clickbait – headlines that often rely on linguistic gimmicks to tempt readers to read further – often did not perform any better and, in some cases, performed worse than traditional headlines. Because fake news is a concern on social media, researchers have explored using AI to systematically identify and block clickbait.

Dealing with misinformation in the digital age is a complex problem. Not only does misinformation have to be identified, tagged, and corrected, but the intent of those responsible for making the claim should also be distinguished. A person may unknowingly spread misinformation, or just be giving their opinion on an issue even though it is later reported as fact. Recently, a team of AI researchers and engineers at Dartmouth created a framework that can be used to derive opinion from "fake news" reports. As ScienceDaily reports, the Dartmouth team's study was recently published in the Journal of Experimental & Theoretical Artificial Intelligence.

You are free to share this article under the Attribution 4.0 International license. With training from humans and machines, an artificial intelligence model can outperform other clickbait detectors, according to new research. In addition, the new AI-based solution was also able to tell the difference between headlines that machines--or bots--generated and ones people wrote, they says. In a study, the researchers asked people to write their own clickbait--an interesting, but misleading, news headline designed to attract readers to click on links to other online stories. The researchers also programmed machines to generate artificial clickbait.

Humans and machines worked together to help train an artificial intelligence -- AI -- model that outperformed other clickbait detectors, according to researchers at Penn State and Arizona State University. In addition, the new AI-based solution was also able to tell the difference between clickbait headlines that were generated by machines -- or bots -- and ones written by people, they said. In a study, the researchers asked people to write their own clickbait -- an interesting, but misleading, news headline designed to attract readers to click on links to other online stories. The researchers also programmed machines to generate artificial clickbaits. Then, the headlines made by both people and machines were used as data to train a clickbait-detection algorithm.

In this post, we'll be doing a step-by-step walkthrough of a basic machine learning project, geared toward people with some knowledge of programming (preferably Python), but who don't have much experience with machine learning. By the end of this post, you'll understand what machine learning is, how it can help you, and be able to build your own machine learning classifiers for any dataset you want. We'll teach a computer how to distinguish between "clickbait" headlines and "normal" headlines, where the former are those irritating "You won't believe what X does to Y" type headlines that deliberately withhold information to try to make people click on the article. Traditionally, to write such a classifier, we would manually inspect hundreds of clickbait headlines and try to identify patterns to differentiate them from "good" headlines. We would then write a script with a lot of hand-crafted rules that tries to discriminate between clickbait headlines and good ones. This is a time consuming process that requires an expert to create the rules, and requires a lot of code maintenance, because we would probably need to continuously update and modify the rules.

The dataset consists of about 12,000 headlines half of which are clickbait. The clickbait headlines were fetched from BuzzFeed, NewsWeek, The Times of India and, The Huffington Post. The genuine/non-clickbait headlines were fetched from The Hindu, The Guardian, The Economist, TechCrunch, The wall street journal, National Geographic and, The Indian Express. Some of the data was from peterldowns's clickbait-classifier repository I used Stanford's Glove Pretrained Embeddings PCA-ed to 30 dimensions. This sped up the training.