TSN4PI框架能追踪社交媒体政治意识形态演变，用PIDN和PIPN两个模块解决数据噪音问题
研究团队提出TSN4PI统一框架，用于追踪社交媒体政治意识形态演变。该框架包含PIDN模块，利用大语言模型和风格迁移处理跨域数据；以及PIPN模块，使用时序图神经网络预测意识形态转变。研究团队发布了两个大型数据集供非商业研究使用。在X和Truth Social平台上的案例研究验证了TSN4PI的有效性。
Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideologies and their temporal dynamics. This task faces challenges such as data scarcity, abundant non-political content, costly and bias-prone manual annotation, and difficulty in modeling future ideological inclinations. To address these issues, we propose TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It includes two core modules. The PIDN uses large language models with style transfer and unsupervised domain adaptation to enable robust ideology detection and filter irrelevant content from noisy, cross-domain data. The PIPN employs temporal graph neural networks to predict future ideological shifts, enabling comprehensive analysis of ideology presence, intensity, and evolution. We release two large-scale datasets for noncommercial research use to facilitate further work. Extensive case studies on multiple platforms (X and Truth Social) validate the effectiveness of TSN4PI and provide empirical insights into political polarization and the evolution of online ideologies. Our findings offer a nuanced perspective, advancing both methodological development and empirical understanding in this field.