Clickbait Detection in Tweets Using Self-attentive Network
This addresses clickbait detection for social media users and platforms, but it is incremental as it builds on existing methods for a specific challenge.
The paper tackled clickbait detection in tweets by reformulating it as a multi-classification problem and using a self-attentive network with biGRUs, achieving first place in the Clickbait Challenge 2017.
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.