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ABSTRACT
Introduction: In the era of information overload, the proliferation of fake news poses a significant challenge to society. This research focuses on developing a robust solution for detecting fake news using machine learning techniques.
Objective: This study aims to enhance the accuracy and efficiency of fake news detection by employing advanced machine learning algorithms. By leveraging the power of artificial intelligence, we seek to contribute to the ongoing efforts in combating misinformation.
Methodology: The research employs a comprehensive dataset comprising both genuine and fabricated news articles. The active voice ensures clarity and precision in conveying the methodology employed.
Feature Extraction: To enhance model performance, relevant features are extracted from the dataset, such as lexical diversity, sentiment analysis, and contextual information. These features serve as crucial input variables for the machine learning models, facilitating the discernment between authentic and fake news.Transition words such as ‘furthermore’ and ‘consequently’ guide the reader through the logical flow of the research process.
Results and Discussion: The findings reveal the effectiveness of the proposed machine learning approach in accurately identifying fake news.
Challenges and Future Work: Addressing challenges related to the dynamic nature of misinformation and evolving tactics employed by malicious actors is essential. Additionally, future work may explore incorporating real-time data streams and continual model training for improved adaptability.
Conclusion: In conclusion, this research contributes to the ongoing efforts in mitigating the spread of fake news by presenting a robust machine learning-based detection system.
