Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance

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 measu...

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Main Authors: ZHANG, Xuan, GAO, Wei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/8320
https://ink.library.smu.edu.sg/context/sis_research/article/9323/viewcontent/PredictingViralRumors_av.pdf
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spelling sg-smu-ink.sis_research-93232023-12-05T03:06:08Z Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance ZHANG, Xuan GAO, Wei 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. 2024-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8320 info:doi/10.1016/j.ipm.2023.103520 https://ink.library.smu.edu.sg/context/sis_research/article/9323/viewcontent/PredictingViralRumors_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Infodemic surveillance Neural multi-task learning Rumor detection User vulnerability Virality prediction Databases and Information Systems Graphics and Human Computer Interfaces Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Infodemic surveillance
Neural multi-task learning
Rumor detection
User vulnerability
Virality prediction
Databases and Information Systems
Graphics and Human Computer Interfaces
Information Security
spellingShingle Infodemic surveillance
Neural multi-task learning
Rumor detection
User vulnerability
Virality prediction
Databases and Information Systems
Graphics and Human Computer Interfaces
Information Security
ZHANG, Xuan
GAO, Wei
Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
description 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.
format text
author ZHANG, Xuan
GAO, Wei
author_facet ZHANG, Xuan
GAO, Wei
author_sort ZHANG, Xuan
title Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
title_short Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
title_full Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
title_fullStr Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
title_full_unstemmed Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
title_sort predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/8320
https://ink.library.smu.edu.sg/context/sis_research/article/9323/viewcontent/PredictingViralRumors_av.pdf
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