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

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Title
Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network
Author(s)
Yoon, Seunghyun; Park, Kunwoo; Lee, Minwoo; 데이터 사이언스 그룹(수리 및 계산과학 연구단); 차미영; Jung, Kyomin
Publication Date
2021
Journal
IEEE ACCESS, v.9, pp.36195 - 36206
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
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.
URI
https://pr.ibs.re.kr/handle/8788114/10612
DOI
10.1109/ACCESS.2021.3062029
ISSN
2169-3536
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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