State of the art in rumor source detection: an in-depth survey

Tracing the truth: a critical review of propagation tracking strategies in social networks

Multimodal Meta-Learning for Early Rumor Detection with Adaptive Decision Timing

Experimental results show that the proposed method significantly reduces detection latency, requiring substantially fewer messages for prediction, while maintaining competitive or superior performance compared to state-of-the-art baselines.

Early detection of online public opinion rumors for cross-event generalization

Results indicate that the proposed SCSNet method is more reliable for unseen-event generalization and sparse early-propagation judgment, and shows consistent performance improvement from 10 to 30 min across repeated runs.

An interpretable multi-scale temporal-structural feature framework for early rumor detection on social media

This work introduces MSTRD, an explainable multi-scale temporal-structural feature framework for rumor detection on social media, and finds that temporal features are the most informative feature group, with a 2.4%-point accuracy drop when removed.

From Noise to Knowledge: Distill Social Media Opinions on Rumor Detection Exploiting LLMs

This work focuses on creating high-quality social media data for rumor detection tasks on the widely popular PHEME-9 dataset, and large language models are used in this work to filter out irrelevant comments prior to the application of machine and deep learning techniques.

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...