Author
Listed:
- Izzat Alsmadi
(Department of Computational, Engineering, and Mathematical Sciences, College of Arts and Science, Texas A&M University, San Antonio, TX 78224, USA
These authors contributed equally to this work.)
- Iyad Alazzam
(Department of Information Systems, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan
These authors contributed equally to this work.)
- Mohammad Al-Ramahi
(Department of Accounting and Finance, College of Business, Texas A&M University, San Antonio, TX 78224, USA
These authors contributed equally to this work.)
- Mohammad Zarour
(Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa 13133, Jordan)
Abstract
Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.
Suggested Citation
Izzat Alsmadi & Iyad Alazzam & Mohammad Al-Ramahi & Mohammad Zarour, 2024.
"Stance Detection in the Context of Fake News—A New Approach,"
Future Internet, MDPI, vol. 16(10), pages 1-16, October.
Handle:
RePEc:gam:jftint:v:16:y:2024:i:10:p:364-:d:1492982
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:364-:d:1492982. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.