Impact of Fake News on Social Media Towards Public Users of Different Age Groups

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Authors: Kahlil bin Abdul Hakim, Sathishkumar Veerappampalayam Easwaramoorthy

Published: 2024-11-08 15:32:20+00:00

AI Summary

This study investigates the effectiveness of machine learning models in detecting fake news, focusing on the performance of Random Forest, SVM, Neural Networks, and Logistic Regression on a Kaggle dataset. SVM and neural networks achieved the highest accuracy (93.29% and 93.69%, respectively), highlighting their potential for fake news detection.

Abstract

This study examines how fake news affects social media users across a range of age groups and how machine learning (ML) and artificial intelligence (AI) can help reduce the spread of false information. The paper evaluates various machine learning models for their efficacy in identifying and categorizing fake news and examines current trends in the spread of fake news, including deepfake technology. The study assesses four models using a Kaggle dataset: Random Forest, Support Vector Machine (SVM), Neural Networks, and Logistic Regression. The results show that SVM and neural networks perform better than other models, with accuracies of 93.29% and 93.69%, respectively. The study also emphasises how people in the elder age group diminished capacity for critical analysis of news content makes them more susceptible to disinformation. Natural language processing (NLP) and deep learning approaches have the potential to improve the accuracy of false news detection. Biases in AI and ML models and difficulties in identifying information generated by AI continue to be major problems in spite of the developments. The study recommends that datasets be expanded to encompass a wider range of languages and that detection algorithms be continuously improved to keep up with the latest advancements in disinformation tactics. In order to combat fake news and promote an informed and resilient society, this study emphasizes the value of cooperative efforts between AI researchers, social media platforms, and governments.

Key findings

SVM and neural networks demonstrated superior performance in fake news detection, achieving accuracies exceeding 93%. The study also highlighted the vulnerability of older adults to fake news due to reduced critical thinking skills. Regularization and dropout techniques improved the neural network's performance by mitigating overfitting.

Approach

The study used a Kaggle dataset containing labeled real and fake news articles. Four machine learning models (Random Forest, SVM, Neural Networks, and Logistic Regression) were trained and evaluated based on accuracy, precision, recall, and F1-score. The neural network model, after hyperparameter tuning, showed the best performance.

Datasets

Kaggle dataset of real and fake news articles (URL provided in paper)

Model(s)

Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression

Author countries

Malaysia

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