Detecting incongruent news headlines with auxiliary textual information

News readers are left with their initial impression gained from its headline, whereas a headline’s purpose is to attract the reader’s attention to the news content. Incongruent news headlines can easily mislead readers with their clickbait headlines, which have become pervasive in online. Thus, cons...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications 2022-08, Vol.199, p.116866, Article 116866
Main Authors: Jang, Joonwon, Cho, Yoon-Sik, Kim, Minju, Kim, Misuk
Format: Article
Language:English
Subjects:
Dataset generation
Datasets
Deep learning
Fake news
Hierarchical attention network
Incongruent headline
News media
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:News readers are left with their initial impression gained from its headline, whereas a headline’s purpose is to attract the reader’s attention to the news content. Incongruent news headlines can easily mislead readers with their clickbait headlines, which have become pervasive in online. Thus, considerable attention has been paid to detect incongruent news headlines before they reach to the readers, however there is still a lack of large-scale dataset which is restricted to title and body text. Accordingly, in this study, we released Incongruent News Headline Dataset, which has been collected and written by one of the largest news media outlets in South Korea. The generated dataset contains additional textual information, such as subtitles, image captions, and other auxiliary information. We proposed a method that effectively detects incongruent news headlines by capturing the complex lexical and contextual textual relationships between a headline and its body using an attention mechanism. The proposed model outperforms the existing models, and we investigated how it fully utilizes all the possible features. •We proposed a model with an attention mechanism for predicting incongruent headlines.•We generated incongruent headline dataset from a real-world news.•We found subtitle and image caption achieves better predictions.•We further investigated the impact of the components in our proposed model.•Our results reveals the possibility of real-world applications.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116866