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DLRD: dual-level network for rumor detection on geo-textual data

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Abstract

The upsurges of location-based social media and geo-location based online social networks have accelerated the spread of rumors, making rumor detection an increasingly important challenge. To tackle this challenge, a variety of conventional and deep learning based approaches are developed to detect rumors based on geo-textual data. This data includes textual, spatial, and temporal information generated from a variety of location-aware social media services. However, the effectiveness of early-stage detection methods based on geo-textual data is limited due to additional information deficiencies, such as the lack of comments and retweets. To address this limitation, we propose a model named Dual-Level Network for Rumor Detection (DLRD). The DLRD model extracts both post-level features and topic-level features. A topic-level network is applied to capture the features of geo-textual data with similar expressions and meanings, such as those that share the same geographic location and time point, indicating that they have a high probability of sharing the same truth value. Specifically, we first leverage the topic modeling method to analyze text content including spatio-temporal information, divide source posts of all events into disjoint topic groups, and then construct a topic-post graph for each group. In the DLRD model, two graph convolutional layers and two full connection layers are employed to learn topic-level and post-level features, respectively. We conduct extensive experiments to compare our model against existing baselines on two public real-world datasets. The experimental results demonstrate that the DLRD model achieves state-of-the-art performance over existing baselines.

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Acknowledgements

This work is supported by the National Science Foundation of China (NSFC No. U2001212, 62032001, and 61932004)

Funding

The authors would like to acknowledge the support provided by the National Science Foundation of China (NSFC No. U2001212, 62032001, and 61932004)

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Authors and Affiliations

  1. School of Computer Science and Engineering & Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hongyu Wang, Ke Li & Shuo Shang

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  1. Hongyu Wang
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  2. Ke Li
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Contributions

All authors contributed to the full manuscript. The first draft of the manuscript was prepared by Hongyu Wang and Ke Li. Specifically, Ke Li provided the conceptual design of this study, Hongyu Wang wrote the methodology part and conducted the experimental study. The remaining parts were organized by Shuo Shang. All authors reviewed previous versions of the manuscript. All authors proofread and approved the final manuscript

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Correspondence to Shuo Shang.

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Wang, H., Li, K. & Shang, S. DLRD: dual-level network for rumor detection on geo-textual data. Geoinformatica 28, 335–351 (2024). https://doi.org/10.1007/s10707-023-00505-5

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