Natural Language Processing (NLP)
Detecting Fake News in Bangla Using Linguistic and Psychological Features
This research introduces a novel psycholinguistic framework for detecting fake news in Bangla. By integrating linguistic and psychological features and utilizing a hybrid CNN-MLP model, we achieved a remarkable accuracy of 96.32%. The study highlights the critical role of language-specific tools and custom lexicons in addressing misinformation in low-resource languages, offering a robust and interpretable solution for content verification.
Executive Impact
Our analysis reveals the direct business advantages of integrating our AI-driven insights:
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Improved Detection Accuracy
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Faster Misinformation Response
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Enhanced Model Interpretability
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Cross-language Adaptability
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Power of NLP in Content Analysis
Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and generate human language. In this study, NLP techniques are crucial for dissecting Bangla text, identifying linguistic patterns, and extracting meaningful features that differentiate authentic from deceptive content. This foundation is essential for developing robust fake news detection systems.
Combating Misinformation with AI
Fake news detection aims to identify false or misleading information to prevent its spread. This research focuses on developing sophisticated AI models, particularly hybrid deep learning architectures, to accurately classify news articles as real or fake in Bangla. By integrating both linguistic and psychological cues, the models achieve high performance, offering a powerful tool in the fight against misinformation.
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Hybrid Model Accuracy for Bangla Fake News Detection
Our hybrid CNN-MLP model, integrating linguistic and psychological features, achieved a high accuracy of 96.32% in detecting fake news in Bangla, demonstrating the strong potential of this approach.
Proposed Methodology for Fake News Detection in Bangla
Input Data (News Articles - Headline and Content)
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Data Preprocessing (HTML, URL, Punctuation, Emoticons, Extra Space, Single Word Removal)
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Feature Extraction (Linguistic & Psychological Features)
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Data Split (Training 80%, Testing 20%)
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Classification (ML, DL, Hybrid Approaches)
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Trained Models
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Performance Metrics
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Prediction (Real/Fake)
The research outlines a systematic methodology for fake news detection, starting from data collection and preprocessing, through feature extraction and model training, to performance evaluation.
| Author | Algorithm | Previous Result | Our Model |
|---|---|---|---|
| Md Gulzar Hussain et al. [22] | SVM | 0.9664 | 0.9724 |
| Md Gulzar Hussain et al. [22] | MNB | 0.9332 | 0.9489 |
| Sultana Umme Habiba [16] | CNN | 0.8570 | 0.9571 |
| Benazir et al. [41] | CNN | 0.9134 | 0.9562 |
Impact of Psycholinguistic Features on Detection
The study demonstrates that integrating linguistic and psychological features provides a more comprehensive understanding of text, which significantly improves model reliability and generalization in fake news detection tasks. Specifically, psychological features (sentiment, emotion, social behavior, swear words) significantly contribute to distinguishing fake from real news.
- Psycholinguistic features enhance model interpretability.
- Custom Bangla lexicons were crucial for capturing language-specific nuances.
- Hybrid CNN-MLP model effectively processes complex linguistic patterns.
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