Tracing Truth and Rumor Diffusions Over Mobile Social Networks: Who are the Initiators?

Qu, Shan; Xu, Hui; Fu, Luoyi; Long, Huan; Wang, Xinbing; Chen, Guihai; Zhou, Chenghu

doi:10.1109/tmc.2021.3119362

Cited by 6 publications

(1 citation statement)

References 71 publications

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“…The explosive spread of rumors threatens the credibility of the internet and has serious adverse effects on individuals and society [9][10][11][12][13][14]. Therefore, effective identification of rumors is crucial to maintaining the security of cyberspace and preserving personal privacy [13,[15][16][17][18][19]. However, rumors' brief and fast-spreading nature make them difficult to detect automatically.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Spatial–Temporal and Knowledge Fusing for Social Media Rumor Detection

Huang

et al. 2023

Electronics

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With the growth of the internet and popularity of mobile devices, propagating rumors on social media has become increasingly easy. Widespread rumors may cause public panic and have adverse effects on individuals. Recently, researchers have found that external knowledge is useful for detecting rumors. They usually use statistical approaches to calculate the importance of different knowledge for the post. However, these methods cannot aggregate the knowledge information most beneficial for detecting rumors. Second, the importance of propagation and knowledge information for discriminating rumors differs among temporal stages. Existing methods usually use a simple concatenation of two kinds of information as feature representation. However, this approach lacks effective integration of propagation information and knowledge information. In this paper, we propose a rumor detection model, Adaptive Spatial-Temporal and Knowledge fusing Network (ASTKN). In order to adaptively aggregate knowledge information, ASTKN employs dynamic graph attention networks encoding the temporal knowledge structure. To better fuse propagation structure information and knowledge structure information, we introduce a new attention mechanism to fuse the two types of information dynamically. Extensive experiments on two public real-world datasets show that our proposal yields significant improvements compared to strong baselines and that it can detect rumors at early stages.

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