|
|||||
|
|
| 面向网络社交媒体的少样本新冠谣言检测 | |
| 引用本文： | 陆恒杨,范晨悠,吴小俊.面向网络社交媒体的少样本新冠谣言检测[J].中文信息学报,2022,36(1):135-144,172. |
| 作者姓名： | 陆恒杨 范晨悠 吴小俊 |
| 作者单位： | 1.江南大学 江苏省模式识别与计算智能工程实验室,江苏 无锡 214122; 2.南京大学 计算机软件新技术国家重点实验室,江苏 南京 210023; 3.深圳市人工智能与机器人研究院,广东 深圳 518129 |
| 基金项目： | 国家自然科学基金(62002137,6201001055);中央高校基本科研业务费专项(JUSRP12021);南京大学计算机软件新技术国家重点实验室开放课题(KFKT2020B02) |
| 摘 要： | 在社交媒体上发布和传播有关新冠的谣言对民生、经济、社会等都产生了严重影响,因此通过机器学习和人工智能技术开展新冠谣言检测具有重要的研究价值和社会意义.现有谣言检测研究,一般假定进行建模和预测的事件已有充足的有标签数据,但对于新冠这类突发事件,由于可训练样本较少,所以此类模型存在局限性.该文聚焦少样本谣言检测问题,旨在使... |
| 关 键 词： | 谣言检测 少样本学习 在线社交媒体 |
Few-shot COVID-19 Rumor Detection for Online Social Media |
|
| LU Hengyang,FAN Chenyou,WU Xiaojun.Few-shot COVID-19 Rumor Detection for Online Social Media[J].Journal of Chinese Information Processing,2022,36(1):135-144,172. | |
| Authors: | LU Hengyang FAN Chenyou WU Xiaojun |
| Affiliation: | 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 |
| 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. |
| Keywords: | rumor detection few-shot learning online social media |
| 点击此处可从《中文信息学报》浏览原始摘要信息 | |
| 点击此处可从《中文信息学报》下载全文 | |