Aug 2026· ACM Transactions on Intelligent Systems and Technology· 0 citations· 89 references
Computer Science
TL;DR
TSN4PI, a unified framework for tracking the evolution of political ideologies on social media, includes two core modules: the PIDN and the PIPN, which employs temporal graph neural networks to predict future ideological shifts, enabling comprehensive analysis of ideology presence, intensity, and evolution.
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.
Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudinal analysis of ideological polarization in Reddit discussions by measuring semantic differences in the language used by opposing political communities. We construct temporally aligned community-specific word embeddings and quantify ideological polarization as the semantic divergence of political concepts over time. Our analysis shows that ideological polarization has increased substantially during the study period, both at the concept- and topic-level. Unlike prior computational work, which has largely focused on cross-sectional analyses or affective dimensions of polarization at a single point at time, our approach captures the evolution of ideological differences in semantic framing. The proposed framework provides a scalable method for studying the temporal dynamics of ideological polarization in large-scale social media discourse.
Roy Yitzchak, Noa Lavie, Ella Rabinovich· 0 citations
Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content. However, existing approaches are typically tailored to platform-specific properties, such as structural affordances or linguistic conventions, which hurts generalizability across platforms. This limitation is increasingly consequential as the social media ecosystem fragments and fringe, alt-tech platforms emerge alongside mainstream ones. We propose a text-based, platform-portable methodology for measuring political partisanship in social media posts, anchored by an external news-credibility signal. Posts are embedded using a transformer-based sentence encoder and clustered into topic groups, which are labeled using the aggregated AllSides media bias scores of cited news outlets. A partisanship axis is then constructed in the embedding space as the difference between centroids of oppositely labeled clusters, and individual posts are scored by projection onto this axis. We apply the method to a corpus of approximately 1.3 million posts collected from Bluesky and Truth Social during the six months preceding the 2024 U.S. presidential election, providing the first cross-platform comparison of partisanship distributions on these two ideologically asymmetric platforms. The resulting partisanship scores correlate significantly with held-out AllSides media bias scores both in-distribution and out-of-distribution on an independent Twitter corpus, and recover within-platform partisan dynamics that platform identity alone cannot explain.
Today most media content is consumed based on algorithmic recommendations. Evidence suggests that this can lead to politically biased media consumption patterns. Automated extraction of political agendas from texts can reveal and analyze political biases in online media -- and thus help fostering politically unbiased media consumption. Here we employ modern political text analysis methods demonstrating the potential of automated fine-grained political bias analysis in online media. We conduct an analysis of political content in German language online media during the period 2019--2022, encompassing several million articles and tweets covering events with profound societal impact globally and nationally, the COVID-19 pandemic and the beginning of the war in Ukraine. Our analysis identifies thematic similarity between national (German and Swiss) reporting, particularly for categories driven by international events. We also find divergences emerging in domestically influenced categories, reflecting differences in national policies and institutional structures. A comparison of newspaper and Twitter discourse reveals that both media converge around a shared core during the pandemic, yet differ in intensity and temporal dynamics. Newspapers exhibit more stable political content, while Twitter reacts through short-lived event-driven spikes. These findings indicate that international crises act as a powerful synchronizing force on political content in classical media, temporarily overriding both national and media-form differences. Our automated political analysis empowers citizens by rendering political agendas in online media transparent. This transparency also enables media outlets to bridge the gap between algorithm-driven echo chambers and a more informed, balanced public discourse.
The rapid growth of social media has transformed political communication by decentralizing information flows and replacing traditional gatekeeping with algorithm-driven content distribution. This shift has significantly influenced democratic participation, opinion formation, electoral mobilization, polarization, and information integrity. This study examines how political narratives are created and amplified through user-generated content, algorithmic curation, and network dynamics. Using a hybrid methodology—combining content analysis, sentiment modeling, network centrality measures, and engagement metrics—the research proposes computational indicators such as the Narrative Amplification Index (NAI), Sentiment Dominance Ratio (SDR), and Polarization Coefficient (PC) to measure narrative influence. Analysis of social media posts during a major electoral cycle reveals that algorithmic amplification strengthens ideological homogeneity and increases cross-group polarization. Approximately 68% of highly engaged political posts contained emotionally charged framing, highlighting the role of affective language in narrative virality. Coordinated networks also contributed significantly to rapid narrative diffusion. The findings demonstrate that social media platforms actively shape political discourse through engagement-driven algorithms, prioritizing emotionally intense and polarizing content. The study provides computational insights into digital political ecosystems and offers implications for policymakers and platform regulators seeking to balance free expression with information integrity
Thomas Fischer, Anna Schmidt· International Journal of Eme...· 0 citations
Abstract This paper advances a methodological framework for the study of policy polarization. We conceptualize polarization as the structure of relationships among political and epistemic actors, measurable through the similarity of their policy statements. To generate these relational data, large language models (LLMs) are employed as scalable tools for extracting, summarizing, and synthesizing policy statements or positions from diverse textual sources, including debates, party platforms, interviews, and think tank publications. The resulting corpus is embedded into a shared vector space, and similarity measures between statements provide the basis for constructing multimodal networks that connect policy statements through semantic similarity and link them to candidates and think tanks. These networks enable the application of social network analysis, such as correspondence analysis, influence modeling, and structural comparison, to model alignments, divergences, and patterns of polarization. The framework contributes a replicable and extensible approach to analyzing policy polarization, integrating computational text processing with network-analytic models to capture the relational dynamics of political discourse.
Dmitry G. Zaytsev, Valentina V. Kuskova, Gregory S. Khvatsky et al.· Network Science· 0 citations
The dissemination of ideological and political discourse on social media platforms often suffers from hidden propagation routes and unquantifiable influence effects. This paper constructs a visualization analysis framework based on multi-source data perception and dynamic graph inference. First, multimodal data from social media platforms are collected, including topic tags, user interaction relationships, text, images, and video comments. A BERT-based model is used for sentiment mining and key node identification. Second, social network analysis and temporal hypergraph modeling are integrated to quantify discourse dissemination paths at macro-network, meso-community, and micro-information-diffusion levels. Third, an interactive dynamic influence graph is built using ECharts and D3.js to present node structure, emotional tendency, community evolution, and propagation trees. Empirical results show that the proposed method achieves an F1 score of 0.914 in path reconstruction, and the correlation coefficient between sentiment quantification and expert ratings exceeds 0.89. The framework effectively reveals the black-box characteristics of discourse dissemination and provides data support for precise ideological and political communication guidance.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
