A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks

With the high-speed development of multimedia technologies, news content is very rich, including not only text but also image information. One of the most essential approaches for detecting fake news is through content analysis. Feature extraction and representation are the crucial steps of the task...

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Bibliographic Details
Published in:Ingenierie des Systemes d'Information
Main Author: Li F.; Rosli M.M.; Wang Y.
Format: Review
Language:English
Published: International Information and Engineering Technology Association 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191560931&doi=10.18280%2fisi.290202&partnerID=40&md5=d07d5eb7999d2c74f47de42501988aee
id 2-s2.0-85191560931
spelling 2-s2.0-85191560931
Li F.; Rosli M.M.; Wang Y.
A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
2024
Ingenierie des Systemes d'Information
29
2
10.18280/isi.290202
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191560931&doi=10.18280%2fisi.290202&partnerID=40&md5=d07d5eb7999d2c74f47de42501988aee
With the high-speed development of multimedia technologies, news content is very rich, including not only text but also image information. One of the most essential approaches for detecting fake news is through content analysis. Feature extraction and representation are the crucial steps of the task. How to accurately characterize news content is still a challenging problem. This article seeks to assist readers comprehend the various strategies connected with feature extraction and representation. Therefore, we scan various digital libraries to find all relevant papers published since 2010. This paper reviews methods that can extract and represent features from three perspectives: text, image, and multi-modal. In particular, we count the usage of these methods in various fake news detection tasks and detail the related theories. We hope that this review can promote the advancement of machine learning, neural networks, and other technologies so as to provide better services for fake news detection. ©2024 The authors.
International Information and Engineering Technology Association
16331311
English
Review

author Li F.; Rosli M.M.; Wang Y.
spellingShingle Li F.; Rosli M.M.; Wang Y.
A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
author_facet Li F.; Rosli M.M.; Wang Y.
author_sort Li F.; Rosli M.M.; Wang Y.
title A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
title_short A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
title_full A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
title_fullStr A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
title_full_unstemmed A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
title_sort A Review of Image and Text Feature Extraction Methods in Fake News Detection Tasks
publishDate 2024
container_title Ingenierie des Systemes d'Information
container_volume 29
container_issue 2
doi_str_mv 10.18280/isi.290202
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191560931&doi=10.18280%2fisi.290202&partnerID=40&md5=d07d5eb7999d2c74f47de42501988aee
description With the high-speed development of multimedia technologies, news content is very rich, including not only text but also image information. One of the most essential approaches for detecting fake news is through content analysis. Feature extraction and representation are the crucial steps of the task. How to accurately characterize news content is still a challenging problem. This article seeks to assist readers comprehend the various strategies connected with feature extraction and representation. Therefore, we scan various digital libraries to find all relevant papers published since 2010. This paper reviews methods that can extract and represent features from three perspectives: text, image, and multi-modal. In particular, we count the usage of these methods in various fake news detection tasks and detail the related theories. We hope that this review can promote the advancement of machine learning, neural networks, and other technologies so as to provide better services for fake news detection. ©2024 The authors.
publisher International Information and Engineering Technology Association
issn 16331311
language English
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record_format scopus
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