A multifaceted GCN-SBERT hybrid model for political polarization detection in online social network

Deep Learning-Based Prediction of Polarization in Social Media Among Youth

The stochastic nature of youth social media polarization poses challenges for traditional early-stage prediction. This study constructs a heterogeneous temporal network to represent multifaceted interactions among users, text, and hashtags, treating the evolution of online opinion as a spatiotemporal signal-propagation...

4HAN: An Enhanced Neural Network for Fake News Detection using Hypergraph

A Four-Level Hierarchical Attention Network (4HAN) that incorporates word-, sentence-, and headline-level attention, along with Hypergraph Convolution and Hypergraph Attention, is proposed using the LIAR dataset and shows a detection accuracy rate of 96.00%, which beats multiple existing methodologies in fake news dete...

Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media

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.

Hybrid Deep Learning Model for Rumor Detection in Social Media

This paper presents a hybrid deep learning approach for rumor detection in social media that combines transformer-based text representation with graph-based modeling of information propagation. The proposed method integrates a Bidirectional Encoder Representations from Transformers model for extracting semantic feature...

Hierarchical attention network for multilingual fake news detection

The experimental results indicate that hierarchical attention mechanisms are effective for multilingual fake news detection, particularly in low-resource multilingual settings.

XY-UP at SSD-2026: Hierarchical Social Support Detection in Spanish Social Media Using Classical and Transformer-Based Approaches