Manuscript received April 28, 2026; accepted May 28, 2026; published June 15, 2026
Abstract—With the widespread adoption of social media, the speed and reach of information dissemination have expanded dramatically in unpredictable ways. This is not merely a simple phenomenon; it is fundamentally changing the way we acquire and process information. Stance detection in social media text is therefore particularly important, especially in rapidly changing and emotionally charged online discussions. Stance detection goes beyond mere sentiment analysis; it involves profoundly analyzing the complex attitudes, stances, and implicit biases embedded in the text. In recent years, with the rapid development of Large Language Models (LLM) and deep learning technologies, traditional stance detection methods have gradually been replaced by more complex and sophisticated techniques, particularly in large-scale text processing, where deep learning plays an increasingly prominent role. This paper will explore the construction of stance data, the evolution of model paradigms, and the issue of generalization in real-world applications. The challenge of stance detection lies not only in accurately identifying stances but also in integrating various factors—such as emotion, culture, and social context—into the model.This paper will analyze this from multiple levels, including data construction and technological changes, and propose new theoretical paths to improve the practicality and credibility of this field.
Keywords—stance detection, social media text, large language models, deep learning
Cite: Jiayi Zhao, "Technical Research on Social Media Text Stance Detection," International Journal of Engineering and Technology, vol. 18, no. 2, pp. 45-48, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0).
