The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and online ideology evolution.
By Yijie Xu, Chao Wang, Hui Xiong
arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.
By Fathima Ameen, Christopher G. Healey
arXiv:2608. 05155v1 Announce Type: cross Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH).
By Maryam Fooladi, Federico Bottino
The study analyzes millions of German-language online articles and tweets from 2019–2022 to uncover political biases using automated text analysis. It finds that international events such as the COVID‑19 pandemic and the Ukraine war create thematic convergence between German and Swiss media, while domestic policy differences drive divergence in locally focused topics. Newspapers maintain more stable political content, whereas Twitter shows rapid, event‑driven spikes, illustrating how media platforms differ in intensity and timing.
By Yara D\"oring, Felix Bie{\ss}mann
arXiv:2507. 03897v3 Announce Type: replace Abstract: We introduce GenAI-Powered Inference (GPI), a statistical framework for both causal and predictive inference using unstructured data, including text and images.
By Kosuke Imai, Kentaro Nakamura
arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.
By Deborah Dore, Elena Cabrio, Serena Villata