RESEARCH ON GENEDER PREDICTION FOR SOCIAL MEDIA USER PROFILING BY MACHINE LEARNING METHOD

Abstract

With the rapid expansion of social media platforms, large volumes of user-generated content are continuously produced, offering valuable insights into user behavior and demographics. However, many users do not explicitly provide personal information such as gender, making it challenging for applications like targeted advertising, recommendation systems, and social analysis. This research focuses on predicting user gender from social media data using machine learning techniques as part of user profiling. The proposed system utilizes textual data such as posts, tweets, comments, and profile descriptions, along with metadata like usernames and activity patterns. Data preprocessing techniques including tokenization, stop-word removal, and feature extraction are applied to convert raw text into structured representations. Methods such as Bag-of-Words, TF-IDF, and word embeddings (e.g., Word2Vec, GloVe, BERT) are used to capture semantic and contextual information. Machine learning algorithms including Naïve Bayes, Support Vector Machines (SVM), Random Forest, and deep learning models are employed to classify users based on gender. Experimental studies show that combining multiple data sources, such as tweets and profile descriptions, significantly improves prediction accuracy. For instance, incorporating profile descriptions can increase accuracy by approximately 10% . Advanced embedding models such as GloVe and transformer-based methods achieve higher performance due to better semantic understanding. Research also indicates that machine learning can predict gender with high accuracy, sometimes exceeding 80–90% depending on data quality and features . Despite promising results, challenges such as data privacy, bias, and ethical concerns remain. Overall, the proposed approach demonstrates the effectiveness of machine learning in gender prediction and contributes to improved user profiling in social media analytics.