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primary topic: Advancing fake news combating using machine learning: a hybrid model approach

Advancing fake news combating using machine learning: a hybrid model approach
type: AcademicArticle
type: Article
abstract: The digital era, while offering unparalleled access to information, has also seen the rapid proliferation of fake news, a phenomenon with the potential to distort public perception and influence sociopolitical events. The need to identify and mitigate the spread of such disinformation is crucial for maintaining the integrity of public discourse. This research introduces a multi-view learning framework that achieves high precision by systematically integrating diverse feature perspectives. Using a diverse dataset of news articles, the approach combines several feature extraction methods, including TF-IDF for individual words (unigrams) and word pairs (bigrams), and counts vectorization to represent text in multiple ways. To capture additional linguistic and semantic information, advanced features, such as readability scores, sentiment scores, and topic distributions generated by latent Dirichlet allocation (LDA), are also extracted. The framework implements a multi-view learning strategy, where separate views focus on basic text, linguistic, and semantic features, feeding into a final ensemble model. Models like logistic regression, random forest, and LightGBM are employed to analyze each view, and a stacked ensemble integrates their outputs. Through rigorous tenfold cross-validation, our proposed multi-view ensemble achieves a state-of-the-art accuracy of 0.9994, outperforming strong baselines, including single-view models and a BERT-based classifier. Robustness testing confirms the model maintains high accuracy even under data perturbations, establishing the value of structured feature separation and intelligent ensemble techniques.
date: 2025-09
is part of: Knowledge and Information Systems
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Advancing fake news combating using machine learning: a hybrid model approach (Texto)
has document: Advancing fake news combating using machine learning: a hybrid model approach (Otro)
subject: Engineering

Advancing fake news combating using machine learning: a hybrid model approach (Texto)
type: Document
has file: s10115-025-02588-y.pdf
has part: s10115-025-02588-y.pdf

Knowledge and Information Systems
type: Collection
issn: 02191377

Malik Muhammad Saad Missen
type: Person
given name: Malik Muhammad Saad
family name: Missen

Mónica Gracia Villar
type: Person
given name: Mónica
family name: Gracia Villar

Arslan Abdul Ghaffar
type: Person
given name: Arslan Abdul
family name: Ghaffar

Arif Mehmood
type: Person
given name: Arif
family name: Mehmood

Zahid Aslam
type: Person
given name: Zahid
family name: Aslam

Eduardo René Silva Alvarado
type: Person
given name: Eduardo René
family name: Silva Alvarado

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Advancing fake news combating using machine learning: a hybrid model approach (Otro)
type: Document
is version of: Advancing fake news combating using machine learning: a hybrid model approach (Texto)
is volatile version of: Advancing fake news combating using machine learning: a hybrid model approach (Texto)
is index codes version of: Advancing fake news combating using machine learning: a hybrid model approach (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

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primary topic: Advancing fake news combating using machine learning: a hybrid model approach

Engineering
type: Concept
preferred label: Ingeniería @es