@article{f93d608f7d1a4d1aaa923aa722f40741,
title = "Detecting High-Engaging Breaking News Rumors in Social Media",
abstract = "Users from all over the world increasingly adopt social media for newsgathering, especially during breaking news. Breaking news is an unexpected event that is currently developing. Early stages of breaking news are usually associated with lots of unverified information, i.e., rumors. Efficiently detecting and acting upon rumors in a timely fashion is of high importance to minimize their harmful effects. Yet, not all rumors have the potential to spread in social media. High-engaging rumors are those written in a manner that ensures achievement of the highest prevalence among the recipients. They are difficult to detect, spread very fast, and can cause serious damage to society. In this article, we propose a new multi-task Convolutional Neural Network (CNN) attention-based neural network architecture to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction in social media. The proposed model learns the salient semantic similarities among important features for detecting high-engaging breaking news rumors and separates them from the rest of the input text. Extensive experiments on five real-life datasets of breaking news suggest that our proposed model outperforms all baselines and is capable of detecting breaking news rumors and predicting their future popularity with high accuracy.",
keywords = "breaking news, deep learning, rumor detection, Social media",
author = "Alkhodair, {Sarah A.} and Fung, {Benjamin C.M.} and Ding, {Steven H. H.} and Cheung, {Kwok Wai} and Huang, {Shih Chia}",
note = "Funding Information: The first author is supported by King Saud University in Riyadh, Saudi Arabia, and the Saudi Arabian Cultural Bureau in Canada. The research was conducted during her doctoral study at Concordia University in Canada. The second author is supported by the Discovery Grants (RGPIN-2018-03872) from the Natural Sciences and Engineering Research Council of Canada (NSERC) and Canada Research Chairs Program (950-232791). Authors{\textquoteright} addresses: S. A. Alkhodair, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia; email:
[email protected]; B. C. M. Fung, School of Information Studies, McGill University, 3661 Peel Street, Montreal, Canada H3A 1X1; email:
[email protected]; S. H. H. Ding, School of Computing, Queen{\textquoteright}s University, 557 Goodwin Hall, Kingston, Canada K7L 2N8; email:
[email protected]; W. K. Cheung, Department of Computer Science, Hong Kong Baptist University, DLB 626, Shaw Campus, Kowloon Tong, Hong Kong; email:
[email protected]; S.-C. Huang, Department of Electronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan; email:
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[email protected]. {\textcopyright} 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2158-656X/2020/12-ART8 $15.00 https://doi.org/10.1145/3416703",
year = "2021",
month = mar,
doi = "10.1145/3416703",
language = "English",
volume = "12",
journal = "ACM Transactions on Management Information Systems",
issn = "2158-656X",
publisher = "Association for Computing Machinery (ACM)",
number = "1",
}