Synergistic Approach for Fake News Detection: Bi-GRUs Coupled with Count Vectorizer and TF-IDFs

Social media has a significant impact on how individuals form their opinions and make decisions. As important as it is to raise awareness, it is also crucial to make sure that the shared content is authentic. Numerous techniques have been used in the past to detect false information on social media,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:SN computer science Jg. 6; H. 5; S. 424
Hauptverfasser: Bellam, Thilak, Prasanna, P. Lakshmi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2025
Springer Nature B.V
Schlagworte:
Accuracy
Adaptability
Algorithms
Artificial intelligence
Artificial neural networks
Blockchain
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Credibility
Data Structures and Information Theory
Datasets
Digital Age
Digital media
Effectiveness
False information
Fraud
Information Systems and Communication Service
Linguistics
Literature reviews
Machine learning
News
Original Research
Pattern Recognition and Graphics
Product reviews
Research Advancements in Intelligent Computing
Sentiment analysis
Social networks
Software Engineering/Programming and Operating Systems
Support vector machines
User generated content
Vision
Fake news detection
Self-attention mechanism
Social media
Count vectorization
Bi-directional gated recurrent units
ISSN:2661-8907, 2662-995X, 2661-8907
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Social media has a significant impact on how individuals form their opinions and make decisions. As important as it is to raise awareness, it is also crucial to make sure that the shared content is authentic. Numerous techniques have been used in the past to detect false information on social media, however they frequently have shortcomings including low effectiveness and overfitting. In order to improve the effectiveness of false news detection, we present a unique method in this paper that combines TF-IDFs and Count Vectorization with Bi-directional Gated Recurrent Units (Bi-GRU). In order to successfully handle the over fitting problem, we also incorporate a Self-Attention technique to retrieve pertinent text features from multiple contexts recorded by Bi-GRU. Using the Buzz News dataset, we assess our method by categorizing news items as authentic or fraudulent. The experimental findings, which yielded an accuracy of 0.8928, show how successful our system is. This is superior to current methods, such as Supervised Artificial Intelligence Algorithms and Deep Neural Networks (DNN), which achieved accuracies of 0.655 and 0.8649 on the Buzz News dataset, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-025-03925-2