2017/10/15 by Yiwei Zhou, Zhou, Yiwei · 4 citations
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1710.05364
openalex publication_date 2017/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.