A novel approach to fake news classification using LSTM-based deep learning models

A novel approach to fake news classification using LSTM-based deep learning models

The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital...

Full description

Bibliographic Details
Main Authors:	Halyna Padalko, Vasyl Chomko, Dmytro Chumachenko
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-01-01
Series:	Frontiers in Big Data
Subjects:	misinformation disinformation fake news deep learning LSTM BiLSTM
Online Access:	https://www.frontiersin.org/articles/10.3389/fdata.2023.1320800/full

Similar Items

Ensemble machine learning approaches for fake news classification
by: Halyna Padalko, et al.
Published: (2023-12-01)

TChecker: A Content Enrichment Approach for Fake News Detection on Social Media
by: Nada GabAllah, et al.
Published: (2023-12-01)

Combating Fake News: Stakeholder Interventions and Potential Solutions
by: Ankur Gupta, et al.
Published: (2022-01-01)

Fake News Explosion in Portugal and Brazil the Pandemic and Journalists’ Testimonies on Disinformation
by: João Canavilhas, et al.
Published: (2022-01-01)

Fake news: a classification proposal and a future research agenda
by: Emad Rahmanian
Published: (2023-04-01)

SA-Bi-LSTM: Self Attention With Bi-Directional LSTM-Based Intelligent Model for Accurate Fake News Detection to Ensured Information Integrity on Social Media Platforms
by: Wang Jian, et al.
Published: (2024-01-01)

Gender Differences in Tackling Fake News: Different Degrees of Concern, but Same Problems
by: Ester Almenar, et al.
Published: (2021-03-01)

Disinformation y Misinformation, Posverdad y Fake News: precisiones conceptuales, diferencias, similitudes y yuxtaposiciones = Disinformation and Misinformation, Post-truth and fake news: conceptual precisions, differences, similarities and juxtapositions
by: Alonso Estrada-Cuzcano, et al.
Published: (2020-06-01)

Libraries and Fake News: What’s the Problem? What’s the Plan?
by: Matthew C Sullivan
Published: (2019-01-01)

Infodemic, disinformation and fake news
by: Jorge Revez, et al.
Published: (2022-03-01)

Surveying fake news: Assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking
by: Andrew P. Weiss, et al.
Published: (2020-02-01)

Information Literacy vs. Fake News: The Case of Ukraine
by: Haigh Maria, et al.
Published: (2019-11-01)

Context-Based Fake News Detection Model Relying on Deep Learning Models
by: Eslam Amer, et al.
Published: (2022-04-01)

Editorial: The psychology of fake news on social media, who falls for it, who shares it, why, and can we help users detect it?
by: David J. Robertson, et al.
Published: (2023-07-01)

Facts, values, and the epistemic authority of journalism: How journalists use and define the terms fake news, junk news, misinformation, and disinformation
by: Farkas Johan, et al.
Published: (2024-04-01)

Detecting Arabic Misinformation Using an Attention Mechanism-Based Model
by: Bashar AlEsawi, et al.
Published: (2024-02-01)

Citizen Perceptions of Fake News in Spain: Socioeconomic, Demographic, and Ideological Differences
by: David Blanco-Herrero, et al.
Published: (2021-08-01)

LSTMCNN: A hybrid machine learning model to unmask fake news
by: Deepali Goyal Dev, et al.
Published: (2024-02-01)

In Singapore’s war on fake news, the Constitution is not an obstacle
by: Lasse Schuldt

Fake News Detection using LSTM based deep learning approach
by: Jaybhaye Sangita M., et al.
Published: (2023-01-01)

Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
by: Muhammad Umer, et al.
Published: (2020-01-01)

Automated Detection of Persuasive Content in Electronic News
by: Brian Rizqi Paradisiaca Darnoto, et al.
Published: (2023-11-01)

FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing Us from Distinguishing True from False News
by: Giancarlo Ruffo, et al.
Published: (2022-09-01)

A Working Definition of Fake News
by: João Pedro Baptista, et al.
Published: (2022-03-01)

Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media
by: Jawaher Alghamdi, et al.
Published: (2023-03-01)

Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM
by: Muhammad Aamir, et al.
Published: (2022-11-01)

Welcome to the Era of Fake News
by: Jonathan Albright
Published: (2017-06-01)

Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area
by: Didit Adytia, et al.
Published: (2023-01-01)

A comparison of artificial intelligence models used for fake news detection
by: Ștefan Emil REPEDE, et al.
Published: (2023-04-01)

Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
by: Antreas Pogiatzis, et al.
Published: (2020-12-01)

Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China
by: Ishan Ayus, et al.
Published: (2023-05-01)

Detection of fake news using deep learning CNN–RNN based methods
by: I. Kadek Sastrawan, et al.
Published: (2022-09-01)

Trends in combating fake news on social media – a survey
by: Botambu Collins, et al.
Published: (2021-04-01)

Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification
by: Jun Xie, et al.
Published: (2019-01-01)

Exploring the Effect of Meteorological Factors on Predicting Hourly Water Levels Based on CEEMDAN and LSTM
by: Zihuang Yan, et al.
Published: (2023-09-01)

Infodemia – an Analysis of Fake News in Polish News Portals and Traditional Media During the Coronavirus Pandemic
by: Malwina Popiołek, et al.
Published: (2021-10-01)

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals
by: Bishwajit Roy, et al.
Published: (2023-06-01)

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects
by: Dhiren Rohera, et al.
Published: (2022-01-01)

Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model
by: Xuchu Jiang, et al.
Published: (2022-06-01)

Fake news and disinformation in Southeast Asia: how should ASEAN respond?
by: Bama Andika Putra, et al.
Published: (2024-03-01)