Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance
Xuan Zhang Wei Gao
In the age of the infodemic, it is crucial to have tools for effectively
monitoring the spread of rampant rumors that can quickly go viral, as well as
identifying vulnerable users who may be more susceptible to spreading such
misinformation. This proactive approach allows for timely preventive measures
to be taken, mitigating the negative impact of false information on society. We
propose a novel approach to predict viral rumors and vulnerable users using a
unified graph neural network model. We pre-train network-based user embeddings
and leverage a cross-attention mechanism between users and posts, together with
a community-enhanced vulnerability propagation (CVP) method to improve user and
propagation graph representations. Furthermore, we employ two multi-task
training strategies to mitigate negative transfer effects among tasks in
different settings, enhancing the overall performance of our approach. We also
construct two datasets with ground-truth annotations on information virality
and user vulnerability in rumor and non-rumor events, which are automatically
derived from existing rumor detection datasets. Extensive evaluation results of
our joint learning model confirm its superiority over strong baselines in all
three tasks: rumor detection, virality prediction, and user vulnerability
scoring. For instance, compared to the best baselines based on the Weibo
dataset, our model makes 3.8\% and 3.0\% improvements on Accuracy and MacF1 for
rumor detection, and reduces mean squared error (MSE) by 23.9\% and 16.5\% for
virality prediction and user vulnerability scoring, respectively. Our findings
suggest that our approach effectively captures the correlation between rumor
virality and user vulnerability, leveraging this information to improve
prediction performance and provide a valuable tool for infodemic surveillance.