Enterprise AI Analysis

The proposed HuEID model introduces an innovative hybrid deep learning approach for cyberbullying detection. It leverages multi-modal social media data, integrating textual data and emojis to capture nuanced expressions.

The methodology involves several key steps:

• Multi-modal Data Collection & Preprocessing: Gathers Urdu text and emojis, then cleans and prepares the data.
• Text Feature Extraction (Bi-LSTM): Utilizes Bidirectional Long Short-Term Memory (Bi-LSTM) networks to capture sequential dependencies and contextual connections in Urdu text, processing both past and future context.
• Emoji Feature Extraction (CNN): Employs Convolutional Neural Networks (CNNs) to extract spatial hierarchies and features from emoji images, classifying them for emotional prediction.
• Feature Fusion: Concatenates the features extracted by Bi-LSTM (hfinal) and CNN (Efinal) into a unified feature vector (UFz), enhancing the model's ability to classify tweets accurately.
• Classification via Transfer Learning (TL): Uses deep Transfer Learning on the unified feature vector (UFz) to improve accuracy and efficiency, leveraging pre-trained networks for robust bullying detection.

This hybrid model effectively handles the linguistic and cultural complexities of Urdu while benefiting from multi-modal data integration.

The HuEID model demonstrates superior performance in identifying cyberbullying instances in Urdu text and emojis. Experiments conducted on two benchmark datasets (DS1 and DS2) yielded impressive results:

• Accuracy: Achieved 94% on DS1 and 97% on DS2.
• F1-Score: Reached 80% on DS1 and 87% on DS2, indicating a strong balance between precision and recall.
• MCC Values: 0.291 for DS1 and 0.343 for DS2, showing performance significantly better than random chance.
• AUC: 0.94 for DS1 and 0.92 for DS2, demonstrating robust discriminatory ability.

When compared to benchmark methods, HuEID showed a 7% improvement in accuracy and a substantial 20% improvement in F1 score. These findings highlight the critical role of multi-modal data and advanced deep learning techniques in enhancing cyberbullying detection, especially for less-resourced languages like Urdu.

The HuEID model offers significant implications for enterprises operating in or serving Urdu-speaking regions:

• Enhanced Content Moderation: Social media platforms can deploy HuEID to automatically and accurately detect cyberbullying in Urdu posts and comments, improving user safety and platform integrity.
• Brand Reputation Management: Companies can protect their brand by swiftly identifying and addressing cyberbullying instances associated with their products or services on Urdu social media.
• User Trust and Engagement: A safer online environment fostered by effective detection systems can lead to increased user trust and engagement, crucial for growth in digital markets.
• Scalability for Multilingual AI: The hybrid deep learning and transfer learning approach provides a blueprint for developing robust cyberbullying detection systems for other low-resource languages, fostering more inclusive global AI solutions.
• Resource Optimization: Automating detection reduces the need for extensive manual review, leading to significant cost savings and more efficient resource allocation for content moderation teams.

By implementing HuEID, enterprises can proactively address a critical social issue while safeguarding their digital presence and fostering healthier online communities.