Classification of X Users based on their Interests Using Deep Learning and Machine Learning Algorithms. A comparative study

Abstract

In today’s digital age, understanding user interests on social media platforms like X is crucial for enhancing user engagement and personalizing content. This study addresses the challenge of classifying X users based on their interests using various machine learning and deep learning algorithms. A comprehensive dataset encompassing diverse interest categories like Politics, Sport, and Health was compiled and preprocessed to ensure data quality and consistency. The study implemented multiple models, including traditional machine learning algorithms (Random Forest, Logistic Regression, Naive Bayes) and deep learning architectures (Convolutional Neural Network, RNN combined with CNN, and

Bidirectional LSTM).

The results demonstrate that while traditional models like Random Forest achieved high accuracy and computational efficiency 94.13%, deep learning models, particularly the Bidirectional LSTM, excelled in capturing complex patterns and contextual information within the data. The Bidirectional LSTM achieved the highest accuracy of 92.54%, albeit with higher computational costs and longer training times. Precision, recall, and F1-score metrics consistently highlighted the strengths of each model, with Random Forest and deep learning models showing robust performance across various evaluation criteria. The study also addressed significant challenges such as data imbalance and overfitting through techniques like data augmentation, regularization, and

hyper-parameter tuning.

Execution time analysis revealed that traditional models are suitable for real-time applications due to their speed especially Naive Bayes, while deep learning models benefit from GPU acceleration to handle larger datasets efficiently. Overall, this comparative analysis underscores the importance of selecting appropriate models based on specific task requirements. The findings suggest that a hybrid approach, leveraging the speed of traditional machine learning models and the advanced pattern recognition capabilities of deep learning models, offers an effective solution for user interest classification on X. This research lays a foundation for developing sophisticated social media analytics tools, contributing

to a deeper understanding of user behavior in the digital age.