Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

  • Seunghyun Yoon Seoul National University
  • Kunwoo Park Korea Advanced Institute of Science and Technology (KAIST)
  • Joongbo Shin Seoul National University
  • Hongjun Lim Korea Advanced Institute of Science and Technology (KAIST)
  • Seungpil Won Seoul National University
  • Meeyoung Cha Korea Advanced Institute of Science and Technology (KAIST)
  • Kyomin Jung Seoul National University


Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world.

AAAI Special Technical Track: AI for Social Impact