# Clickbait Detection in Tweets Using Self-attentive Network

arXiv: https://arxiv.org/abs/1710.05364 · HTML: https://mlsanity.com/paper/1710.05364/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [Prompt-tuning for Clickbait Detection via Text Summarization](https://mlsanity.com/paper/2404.11206/) (2024)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (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)
- [Te Ahorr'e Un Click: A Revised Definition of Clickbait and Detection in Spanish News](https://mlsanity.com/paper/2507.09777/) (2025)

## Abstract (arXiv)

Clickbait detection in tweets remains an elusive challenge. In this paper, we describe the solution for the Zingel Clickbait Detector at the Clickbait Challenge 2017, which is capable of evaluating each tweet's level of click baiting. We first reformat the regression problem as a multi-classification problem, based on the annotation scheme. To perform multi-classification, we apply a token-level, self-attentive mechanism on the hidden states of bi-directional Gated Recurrent Units (biGRU), which enables the model to generate tweets' task-specific vector representations by attending to important tokens. The self-attentive neural network can be trained end-to-end, without involving any manual feature engineering. Our detector ranked first in the final evaluation of Clickbait Challenge 2017.