Natural Language Processing Techniques for Misinformation Detection in Social Media: A Comparative Analysis
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Natural Language Processing Techniques for Misinformation Detection in Social Media: A Comparative Analysis
Dr. Sr. Mini T. V 1*, Kochumol Abraham2 , Julie P. A 3, Sreelakshmi 4, Aleena Rose Jacob 5
1 Associate Professor, Department of Computer Science
Sacred Heart College (Autonomous), Chalakudy, Kerala, India
2 Assistant Professor ,PG Department of Computer Applications ,
Marian College Kuttikkanam (Autonomous), Kuttikkanam, Kerala, India.
3,4,5 Assistant Professor, Department of Computer Science
Sacred Heart College (Autonomous), Chalakudy, Kerala, India
Abstract - This paper investigates effective techniques for automatically identifying misinformation on social media platforms using natural language processing (NLP) and machine learning approaches. The proliferation of false information across digital channels presents significant challenges to information integrity and public discourse. We examine multiple classifier architectures Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Transformer-based models—for classifying content as reliable or unreliable. Our analysis incorporates linguistic features, contextual embeddings, and user engagement patterns to create robust detection systems. Performance is evaluated using precision, recall, F1-score, and accuracy metrics across multiple benchmark datasets. Results indicate that hybrid approaches combining textual analysis with metadata features achieve superior discriminative capability, with BERT-based models demonstrating the highest accuracy (93.5%) when augmented with user interaction signals. This research contributes to the development of automated systems that can help mitigate the spread of misinformation in digital environments.
Key Words: Natural Language Processing, Misinformation Detection, Social Media Analysis, Machine Learning Classifiers, Transformer Models, Content Verification.
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