NLP & SOCIAL MEDIA MODERATION
Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text
This study introduces a new model for detecting insults in Roman Urdu, filling an important gap in natural language processing (NLP) for low-resource languages. The transliterated nature of Roman Urdu also poses specific challenges from a computational linguistics perspective, including non-standardized grammar, variation in spellings for the same word, and high levels of code-mixing with English, which together make automated insult detection for Roman Urdu a highly complex problem. To address these problems, we created a large-scale dataset with 46,045 labeled comments from social media websites such as Twitter, Facebook, and YouTube. This is the first dataset for insult detection for Roman Urdu that was created and annotated with insulting and non-insulting content. Advanced preprocessing methods such as text cleaning, text normalization, and tokenization are used in the study, as well as feature extraction using TF-IDF through unigram (Uni), bigram (Bi), trigram (Tri), and their unions: Uni+Bi+Trigram. We compared ten machine learning algorithms (logistic regression, support vector machines, random forest, gradient boosting, AdaBoost, and XGBoost) and three deep learning topologies (CNN, LSTM, and Bi-LSTM). Different models were compared, and ensemble ones were proven to give the highest F1-scores, reaching 97.79%, 97.78%, and 95.25%, respectively, for AdaBoost, decision tree, TF-IDF, and Uni+Bi+Trigram configurations. Deeper learning models also performed on par, with CNN achieving an F1-score of 97.01%. Overall, the results highlight the utility of n-gram features and the combination of robust classifiers in detecting insults. This study makes strides in improving NLP for Roman Urdu, yet further research has established the foundation of pre-trained transformers and hybrid approaches; this could overcome existing systems and platform limitations. This study has conscious implications, mainly on the construction of automated moderation tools to achieve safer online spaces, especially for South Asian social media websites.
Executive Impact
Our analysis reveals the transformative potential of this research for enterprises aiming to enhance online safety and content moderation efficiency.
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Max F1-Score Achieved
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Dataset Comments Analyzed
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Social Media Sources
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Top DL Model F1-Score (CNN)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Raw Comments Collection
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Dataset Preparation & Annotation
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Data Preprocessing (Cleaning, Norm.)
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Feature Extraction (TF-IDF N-grams)
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ML & DL Model Training
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Insult Detection Output
The methodology outlines a comprehensive approach from raw data collection to refined insult detection. This structured process ensures robustness and adaptability, critical for enterprise-scale NLP solutions in challenging linguistic contexts like Roman Urdu.
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Highest F1-Score Achieved (AdaBoost, Uni+Bi+Trigram Features)
The highest F1-score of 97.79% demonstrates exceptional accuracy in identifying insults within Roman Urdu text. This level of precision is crucial for automated content moderation, minimizing false positives and negatives, and ensuring effective filtering of harmful online discourse.
The research highlights that ensemble methods like AdaBoost and Decision Trees, when combined with rich n-gram features, are particularly effective. This signifies that a blended approach to feature engineering and model selection yields superior results, offering a scalable solution for complex linguistic challenges.
| Model Type | Model | Vectorization/Embeddings | F1-Score | Key Strengths for Enterprise |
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| Machine Learning | AdaBoost | TF-IDF Uni+Bi+Trigram | 97.79% |
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| Machine Learning | Decision Tree | TF-IDF Uni+Bi+Trigram | 97.78% |
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| Deep Learning | CNN | Pre-trained Embeddings | 97.01% |
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| Deep Learning | LSTM | Pre-trained Embeddings | 95.78% |
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This comparison highlights that while advanced deep learning models like CNNs offer competitive performance, well-tuned ensemble machine learning models, especially with comprehensive feature sets like Uni+Bi+Trigram TF-IDF, can achieve slightly superior F1-scores for this specific task in Roman Urdu, making them highly viable for enterprise deployment.
Real-World Impact: Enhancing South Asian Social Media Moderation
This research has direct implications for creating safer online environments. With the proliferation of user-generated content in Roman Urdu on platforms like Twitter, Facebook, and YouTube, automated insult detection systems are crucial. The high F1-scores achieved, particularly by ensemble ML models and CNNs, demonstrate the feasibility of developing robust moderation tools. These tools can significantly reduce the workload on human moderators, ensure more consistent policy enforcement, and mitigate psychological distress caused by harmful content. This is especially vital for South Asian social media users, who often lack language-specific moderation solutions, fostering healthier digital communication.
For enterprises operating social media platforms or managing user-generated content in the South Asian context, integrating such AI-powered moderation can lead to:
- Increased User Engagement: A safer environment encourages more positive interaction.
- Brand Protection: Reduced harmful content safeguards brand reputation.
- Operational Efficiency: Automating detection frees up human resources for more complex cases.
- Regulatory Compliance: Adherence to content guidelines in local languages.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings by deploying advanced AI for insult detection within your enterprise.
Estimated Annual Savings
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Annual Moderator Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating insult detection AI into your enterprise workflow.
Phase 1: Discovery & Strategy
Initial consultation to understand your specific content moderation challenges, platform specifics, and linguistic requirements for Roman Urdu. Define project scope, KPIs, and success metrics.
Phase 2: Data & Model Customization
Leverage existing Roman Urdu dataset and fine-tune our hybrid ML/DL models with your proprietary data if available. Develop custom preprocessing pipelines to match your content's unique characteristics.
Phase 3: Integration & Testing
Seamless integration of the insult detection API into your current moderation tools or platform. Rigorous A/B testing and quality assurance to ensure accuracy and minimize disruption.
Phase 4: Deployment & Optimization
Full deployment of the AI system, followed by continuous monitoring and iterative optimization based on real-world performance and feedback. Provide ongoing support and model updates.
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