This is the first systematic integration of psycholinguistic theory with deep learning for Bangla fake news detection, enabling explainable predictions while maintaining state-of-the-art performance.

Features Extracted

Psycholinguistic Features (17):

Emotional intensity markers (fear, anger, positive/negative sentiment)
Uncertainty and hedging patterns
Cognitive load indicators (repetition, disfluency)
Deception-specific linguistic patterns (self-reference, present tense usage)

Discourse Features (5):

Text coherence scores across paragraphs
Argumentative structure analysis (claims vs evidence)
Topic progression and transition patterns

Model Architecture

The model integrates:

BERT contextual embeddings (768 dimensions)
Psycholinguistic features (17 dimensions)
Discourse features (5 dimensions)
Feature fusion layer
Final classification head

Usage

python

# Note: This model requires custom feature extraction
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("NUHASHROXME/bangla-fake-news-interpretable")
# Custom model loading code required for interpretable features