Fake News Prediction Using Hybrid Model-Systematic Literature Review

The proliferation of fake news is recognized as a deliberate strategy to manipulate and misinform audiences, thereby undermining the consumption of authentic information. The repercussions of this phenomenon span from mere annoyance to the significant potential for distorting societal and even natio...

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Published in:2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
Main Author: Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176554559&doi=10.1109%2fAiDAS60501.2023.10284628&partnerID=40&md5=a5ad08f8b3cd634334b55d688efc56f5
id 2-s2.0-85176554559
spelling 2-s2.0-85176554559
Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
Fake News Prediction Using Hybrid Model-Systematic Literature Review
2023
2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings


10.1109/AiDAS60501.2023.10284628
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176554559&doi=10.1109%2fAiDAS60501.2023.10284628&partnerID=40&md5=a5ad08f8b3cd634334b55d688efc56f5
The proliferation of fake news is recognized as a deliberate strategy to manipulate and misinform audiences, thereby undermining the consumption of authentic information. The repercussions of this phenomenon span from mere annoyance to the significant potential for distorting societal and even national perspectives. The contemporary technological landscape has expedited the dissemination of information, underscoring the urgency of discerning evolving techniques in fake news detection. In the context of prevalent social media platforms, a sole reliance on content-based methodologies proves inadequate. This study employs a systematic literature review following the PRISMA protocol to illuminate the contemporary landscape of fake news detection methodologies. The investigation reveals a spectrum of strategies categorized under the rubric of hybrid models, wherein multiple features or models are amalgamated. The discerned hybrid models exhibit a diversity of methodologies, coalescing into two overarching paradigms: the fusion of diverse features and the integration of multiple models. The former primarily encompasses composite feature-based ensembles, often amalgamating content-based features with complementary attributes. The latter paradigm predominantly entails the synthesis of various deep learning approaches, culminating in enhanced performance metrics for fake news detection. The synthesized findings not only provide a comprehensive overview of the prevalent hybrid approaches but also establish a benchmark for forthcoming researchers embarking on predictive endeavors involving hybrid methodologies. © 2023 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
spellingShingle Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
Fake News Prediction Using Hybrid Model-Systematic Literature Review
author_facet Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
author_sort Elias M.I.; Mahmud Y.; Mutalib S.; Kamaliah Kamarudin S.N.; Maskat R.; Rahman S.A.
title Fake News Prediction Using Hybrid Model-Systematic Literature Review
title_short Fake News Prediction Using Hybrid Model-Systematic Literature Review
title_full Fake News Prediction Using Hybrid Model-Systematic Literature Review
title_fullStr Fake News Prediction Using Hybrid Model-Systematic Literature Review
title_full_unstemmed Fake News Prediction Using Hybrid Model-Systematic Literature Review
title_sort Fake News Prediction Using Hybrid Model-Systematic Literature Review
publishDate 2023
container_title 2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/AiDAS60501.2023.10284628
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176554559&doi=10.1109%2fAiDAS60501.2023.10284628&partnerID=40&md5=a5ad08f8b3cd634334b55d688efc56f5
description The proliferation of fake news is recognized as a deliberate strategy to manipulate and misinform audiences, thereby undermining the consumption of authentic information. The repercussions of this phenomenon span from mere annoyance to the significant potential for distorting societal and even national perspectives. The contemporary technological landscape has expedited the dissemination of information, underscoring the urgency of discerning evolving techniques in fake news detection. In the context of prevalent social media platforms, a sole reliance on content-based methodologies proves inadequate. This study employs a systematic literature review following the PRISMA protocol to illuminate the contemporary landscape of fake news detection methodologies. The investigation reveals a spectrum of strategies categorized under the rubric of hybrid models, wherein multiple features or models are amalgamated. The discerned hybrid models exhibit a diversity of methodologies, coalescing into two overarching paradigms: the fusion of diverse features and the integration of multiple models. The former primarily encompasses composite feature-based ensembles, often amalgamating content-based features with complementary attributes. The latter paradigm predominantly entails the synthesis of various deep learning approaches, culminating in enhanced performance metrics for fake news detection. The synthesized findings not only provide a comprehensive overview of the prevalent hybrid approaches but also establish a benchmark for forthcoming researchers embarking on predictive endeavors involving hybrid methodologies. © 2023 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
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