A new combined data-preprocessing and machine learning approach for emotion-based fake news detection DOI Creative Commons

Belkacem Mostefai,

Tarek Boutefara,

Nada Khedimallah

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e12309 - e12309

Published: Dec. 17, 2024

The proliferation of social network platforms, especially post-COVID, has accelerated the spread fake news, impacting politics, public health, and stability. This widespread issue undermines trust, polarizes communities, influences decision-making, highlighting need for innovative solutions automatic detection mitigation. paper introduces a new combined data-preprocessing machine learning approach emotion-based news detection. A key focus study is emotional dimension identified as critical factor influencing its propagation on networks. By analyzing cues embedded in content, proposed method seeks to detect based characteristics. To validate this approach, two classification methods K-Nearest Neighbors (KNN) Support Vector Machines (SVM) were tested, achieving accuracy rates 65% 70%, respectively. These findings demonstrate potential analysis enhancing frameworks suggest avenues further refinement broader application.

Language: Английский

Fake news, real needs: A qualitative study on Sino-Japanese theurgy fighting DOI Creative Commons

清青 高,

Qianqian Fu

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42255 - e42255

Published: Jan. 24, 2025

Language: Английский

Citations

0
A new combined data-preprocessing and machine learning approach for emotion-based fake news detection DOI Creative Commons

Belkacem Mostefai,

Tarek Boutefara,

Nada Khedimallah

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e12309 - e12309

Published: Dec. 17, 2024

The proliferation of social network platforms, especially post-COVID, has accelerated the spread fake news, impacting politics, public health, and stability. This widespread issue undermines trust, polarizes communities, influences decision-making, highlighting need for innovative solutions automatic detection mitigation. paper introduces a new combined data-preprocessing machine learning approach emotion-based news detection. A key focus study is emotional dimension identified as critical factor influencing its propagation on networks. By analyzing cues embedded in content, proposed method seeks to detect based characteristics. To validate this approach, two classification methods K-Nearest Neighbors (KNN) Support Vector Machines (SVM) were tested, achieving accuracy rates 65% 70%, respectively. These findings demonstrate potential analysis enhancing frameworks suggest avenues further refinement broader application.

Language: Английский

Citations

0