Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 86
  • Download : 0
This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-03
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.9, pp.36195 - 36206

ISSN
2169-3536
DOI
10.1109/ACCESS.2021.3062029
URI
http://hdl.handle.net/10203/281592
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0