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| Few-shot COVID-19 Rumor Detection for Online Social Media |
| LU Hengyang1,2, FAN Chenyou3, WU Xiaojun1 |
1.Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, Jiangsu 214122, China;
2.National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210023, China;
3.Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518129, China |
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Abstract The COVID-19 rumors published and spread on the online social media have a serious impact on people's livelihood, economy, and social stability. Most existing researches for rumor detection usually assumed that the happened events for modeling and predictions already have enough labeled data. These studies have severe limitations on detecting emergent events such as the COVID-19 which has very few training instances. This article focuses on the problem of few-shot rumor detection, aiming to detect rumors of emergent events with only very few labeled instances. Taking the COVID-19 rumors from Sina Weibo as the target, we construct a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection, and propose a deep neural network based few-shot rumor detection model with meta learning. In the few-shot machine learning scenarios, the experimental results of the proposed model on the COVID-19 rumor dataset and the PHEME public dataset have been significantly improved.
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Received: 29 June 2021
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2022, Vol. 36 
