Clickbait Detection in Tweets Using Self-attentive Network

Abstract

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.

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• A Structured Self-attentive Sentence Embedding
• The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
• Learning to Identify Ambiguous and Misleading News Headlines
• Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?

Cited by in corpus (8)

• Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
• BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset
• Federated Hierarchical Hybrid Networks for Clickbait Detection
• Using Neural Network for Identifying Clickbaits in Online News Media
• SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?
• Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection
• Is it a click bait? Let's predict using Machine Learning
• Clickbait in Hindi News Media : A Preliminary Study