Intelligent Logical Fallacies Detection System

Project Overview

The Intelligent Logical Fallacies Detection System represents a cutting-edge application of natural language processing and machine learning to automatically identify logical fallacies in argumentative text. This system addresses the growing need for automated fact-checking and argument analysis in an era of information overload and misinformation.

Logical fallacies are common errors in reasoning that undermine the validity of arguments. By developing an AI system capable of detecting these fallacies, we can enhance critical thinking tools, improve educational resources, and support more informed public discourse. The project demonstrates the practical application of state-of-the-art NLP techniques to solve real-world problems in argumentation and logic.

Technologies Used

NLP NLTK TensorFlow PyTorch Deep Learning Transformers BERT Python Scikit-learn

We leveraged TensorFlow and PyTorch for their robust deep learning capabilities and extensive NLP libraries. BERT and other transformer models provided state-of-the-art language understanding, while NLTK handled traditional NLP preprocessing tasks. The combination of these technologies enabled both classical and modern approaches to text analysis and classification.

Targeted Logical Fallacies

Common Fallacies Detected by the System:

Ad Hominem: Attacking the person making the argument rather than the argument itself
Straw Man: Misrepresenting someone's argument to make it easier to attack
False Dilemma: Presenting only two options when more exist
Slippery Slope: Assuming one event will lead to extreme consequences
Circular Reasoning: Using the conclusion as evidence for the argument
Appeal to Authority: Using authority as evidence when the authority lacks expertise
Red Herring: Introducing irrelevant information to distract from the main argument

Project Gallery

Model Performance Metrics

Accuracy, precision, and recall charts for different fallacy types

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Model Architecture Diagram

Neural network architecture and data flow visualization

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Web Interface Demo

User interface for testing fallacy detection on custom text

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Dataset Visualization

Distribution of fallacy types in training dataset

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Live Detection Demo

Real-time fallacy detection on sample arguments

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Feature Engineering Pipeline

Text preprocessing and feature extraction workflow

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Technical Implementation

Architecture & Design

The system employs a multi-stage architecture combining traditional NLP preprocessing with modern transformer-based models. The pipeline includes text normalization, tokenization, feature extraction, and classification using an ensemble of BERT-based models fine-tuned for fallacy detection.

Key Features

Multi-class Classification: Simultaneously detects multiple types of logical fallacies with confidence scores for each category
Context-Aware Analysis: Uses BERT embeddings to understand contextual relationships and subtle linguistic patterns indicative of fallacious reasoning
Ensemble Learning: Combines multiple model architectures including CNN, LSTM, and transformer models for robust performance
Explainable AI: Provides attention visualizations and feature importance scores to explain detection decisions

Data Processing Pipeline

The text processing pipeline includes advanced preprocessing steps: sentence segmentation, dependency parsing, named entity recognition, and sentiment analysis. These features are combined with BERT embeddings to create rich representations that capture both syntactic and semantic patterns associated with different fallacy types.

Challenges & Solutions

Challenge 1: Limited Training Data

High-quality labeled datasets for logical fallacies are scarce and expensive to create. We addressed this through data augmentation techniques including paraphrasing, back-translation, and synthetic example generation using GPT-based models. We also employed transfer learning from pre-trained language models to leverage general language understanding.

Challenge 2: Contextual Ambiguity

Many statements can be fallacious or valid depending on context, making classification challenging. Our solution involved developing context-aware features that consider surrounding sentences, discourse markers, and argumentative structure. We also implemented uncertainty quantification to flag ambiguous cases for human review.

Challenge 3: Class Imbalance and Overlap

Some fallacy types are rare in natural text, while others frequently co-occur, creating classification challenges. We employed focal loss functions to handle class imbalance, used multi-label classification to handle overlapping fallacies, and implemented class-specific data sampling strategies during training.

Results & Impact

Performance Metrics

Achieved 87% overall accuracy across 12 different fallacy types
Obtained 92% precision for common fallacies (Ad Hominem, Straw Man)
Demonstrated 85% recall for identifying fallacious arguments vs. valid ones
Processed text inputs in real-time with sub-second response times
Successfully validated on cross-domain datasets (political debates, academic arguments)

Learning Outcomes

This project provided deep insights into advanced NLP techniques, transformer architectures, and the challenges of building practical AI systems for complex reasoning tasks. I gained expertise in handling imbalanced datasets, implementing attention mechanisms, and developing explainable AI systems. The project also enhanced my understanding of argumentation theory and critical thinking principles.

Potential Applications

The system has broad applications in education (teaching critical thinking), journalism (fact-checking assistance), social media monitoring (detecting misleading arguments), and legal analysis (identifying weak reasoning in legal documents). Future work could extend to multilingual fallacy detection and integration with automated debate systems.

Links & Resources

View Project Details Source Code Live Demo

Additional Resources

Dataset & Methodology

Data Collection

We curated a comprehensive dataset from multiple sources including academic papers, online debates, social media discussions, and educational resources. The dataset contains over 15,000 labeled examples covering 12 different fallacy types, with careful attention to balanced representation and quality control.

Annotation Process

Multiple expert annotators with backgrounds in logic and philosophy labeled the data using detailed guidelines. Inter-annotator agreement was measured using Cohen's kappa, achieving 0.78 agreement for binary fallacy detection and 0.65 for specific fallacy type classification.

Model Training

We employed transfer learning starting from pre-trained BERT models, fine-tuning on our fallacy detection task. The training process included curriculum learning, starting with clear examples and gradually introducing more ambiguous cases. Cross-validation and holdout testing ensured robust performance evaluation.

Technical Specifications

Model Architecture

Base Model: BERT-large with 24 layers, 1024 hidden units
Classification Head: Multi-layer perceptron with dropout regularization
Input Processing: Maximum sequence length of 512 tokens
Output: Multi-class probabilities for 12 fallacy types + valid argument
Training Data: 15,000+ labeled examples across multiple domains

Performance Requirements

Inference Time: < 200ms per text sample (CPU), < 50ms (GPU)
Memory Usage: 2GB RAM for model loading, 500MB additional per batch
Accuracy Target: > 85% overall accuracy, > 90% for common fallacies
Scalability: Supports batch processing of up to 1000 texts simultaneously