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

Year: 2025

Type: article

Abstract: 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.

Source: Global Media and Social Sciences Research Journal

DOI: 10.71465/gmssrj77

Author: MingXiang Lin

Language: English

Topic: LGBTQ Health, Identity, and Policy

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Gender equality

Open Access status: hybrid

OpenAlex ID: W4413280643

FWCI:

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