Prediction of Gender Identity Trends in the United States Based on Social Media Data

This study explores gender identity trends through the analysis of social media data in the United States, using a systematic approach of data collection, preprocessing, statistical analysis, and predictive modeling. Descriptive statistics show that discussions related to non-traditional gender identity continue to increase, and keywords show a dynamic pattern influenced by social events. A model based on long-short-term memory networks is used to predict future trends, indicating that the attention paid to the topic will continue to grow. The results highlight the value of social media as a mirror reflecting social attitudes, provide insights into cultural changes, and support decision-making in related fields.