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Related Experiment Video

Updated: Aug 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Multimodal Fake-News Recognition Using Ensemble of Deep Learners.

Abdulhameed Al Obaid1,2, Hassan Khotanlou1, Muharram Mansoorizadeh1

  • 1RIV Lab, Department of Computer Engineering, Bu-Ali Sina University, Hamedan 65178-38695, Iran.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

Early detection of fake news is vital. This study introduces an improved deep learning ensemble model that enhances fake news detection accuracy by focusing on diverse content elements.

Keywords:
attention mechanismensemble of deep learnersfake-news recognitionmultimodal data

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Social networks accelerate information dissemination, often including multimedia, increasing user engagement.
  • The rapid spread of misinformation via social media necessitates effective fake news detection systems.
  • Deep learning models are preferred for fake news recognition due to the limitations of manual feature extraction.

Purpose of the Study:

  • To enhance the performance of existing fake news detection approaches.
  • To develop an ensemble of deep learners that effectively utilize attention mechanisms.
  • To introduce a novel loss function promoting learner diversity and classification accuracy.

Main Methods:

  • An ensemble of deep learning models was utilized, incorporating attention mechanisms.
  • A novel loss function was proposed to encourage learners to focus on different news content segments.
  • Learners shared a common deep-feature extractor, differing only in their attention modules, to reduce parameters and prevent overfitting.

Main Results:

  • The proposed ensemble model demonstrated superior performance compared to existing methods.
  • Experiments on widely used fake news detection datasets validated the effectiveness of the approach.
  • The novel loss function successfully promoted diversity among learners while maintaining high classification accuracy.

Conclusions:

  • The developed ensemble deep learning model with a novel loss function offers a significant improvement for fake news detection.
  • The method effectively addresses the challenge of misinformation spread on social media platforms.
  • The approach provides a more efficient and robust solution for automatic fake news identification.