Problem
Research questions and friction points this paper is trying to address.
political ideologies
social media
data scarcity
non-political content
ideological shifts
Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
🤖 AI Summary
本文提出TSN4PI框架，利用语言模型和时序图神经网络解决社交媒体上政治意识形态演变追踪问题，包括检测和预测意识形态变化。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
TSN4PI
PIDN
PIPN
Temporal Graph Neural Networks
Unsupervised Domain Adaptation
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