Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning

被引：3

作者：

Liu, Yudong ^{[1
,2
]}

Yang, Xiaoyu ^{[1
]}

Zhang, Xi ^{[1
]}

Tang, Zhihao ^{[1
]}

Chen, Zongyi ^{[1
]}

Liwen, Zheng ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv MoE, Beijing, Peoples R China

[2] Beijing Elect & Sci Technol Inst, Beijing, Peoples R China

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2022, PT III | 2022年

关键词：

Rumor veracity; Stance classification; Multi-task learning; Graph neural network;

D O I：

10.1007/978-3-031-00129-1_16

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Previous studies have shown that the multi-task learning paradigm with the stance classification could facilitate the successful detection of rumours, but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel Multi-Task Learning framework with Shared Multi-channel Interactions (MTL-SMI), which is composed of two shared channels and two task-specific graph channels. The shared channels extract task-invariant text features and structural features, and the task-specific graph channels, by interacting with the shared channels, extract the task-enhanced structural features. Experiments on two realworld datasets show the superiority of MTL-SMI against strong baselines.

引用

页码：230 / 237

页数：8

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