# Clickbait Detection via Large Language Models

arXiv: https://arxiv.org/abs/2306.09597 · HTML: https://mlsanity.com/paper/2306.09597/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder](https://mlsanity.com/paper/1811.07066/) (2018)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!](https://mlsanity.com/paper/1612.01340/) (2016)
- [Fake News Detection as Natural Language Inference](https://mlsanity.com/paper/1907.07347/) (2019)
- [Learning to Identify Ambiguous and Misleading News Headlines](https://mlsanity.com/paper/1705.06031/) (2017)
- [Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks](https://mlsanity.com/paper/1710.01507/) (2017)
- [MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching](https://mlsanity.com/paper/2010.03617/) (2020)

## Built on by (most cited first, 4 of 4)

- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics](https://mlsanity.com/paper/2507.12372/) (2025)
- [LLM-guided headline rewriting for clickability enhancement without clickbait](https://mlsanity.com/paper/2603.22459/) (2026)
- [Clickbait detection: quick inference with maximum impact](https://mlsanity.com/paper/2604.08148/) (2026)

## Abstract (arXiv)

Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success in a series of NLP downstream tasks. However, it is not yet known whether LLMs can be served as a high-quality clickbait detection system. In this paper, we analyze the performance of LLMs in the few-shot and zero-shot scenarios on several English and Chinese benchmark datasets. Experimental results show that LLMs cannot achieve the best results compared to the state-of-the-art deep and fine-tuning PLMs methods. Different from human intuition, the experiments demonstrated that LLMs cannot make satisfied clickbait detection just by the headlines.