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- 电子邮箱：qiany@uestc.edu.cn
- 入职时间：2000-07-01
- 所在单位：经济与管理学院
- 学历：博士研究生毕业
- 办公地点：Room C217, SEM Building, Qingshuihe Campus, UESTC
- 性别：女
- 联系方式：qiany@uestc.edu.cn
- 学位：管理学博士学位
- 职称：教授
- 在职信息：在职人员
- 毕业院校：电子科技大学
- 博士生导师
- 曾获荣誉：四川省科技进步奖一等奖，电子科技大学“成电创新教学示范奖”，优秀硕士学位论文指导教师奖。
- 学科：管理科学与工程
Improving fake news detection with domain-adversarial and graph-attention neural network
- 点击次数：
- 所属单位：[1]Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China;[2]Yunnan Univ Finance & Econ, Sch Business, Kunming 650221, Yunnan, Peoples R China
- 发表刊物：DECISION SUPPORT SYSTEMS
- 关键字：Fake news detection; Feature extraction; Adversarial neural network; Graph-attention network
- 摘要：With the widespread use of online social media, we have witnessed that fake news causes enormous distress and inconvenience to people＇s social life. Although previous studies have proposed rich machine learning methods for identifying fake news in social media, the task of detecting fake news in emerging news events/domains remains a challenging problem due to the wide range of news topics on social media as well as the evolution and variation of fake news contents in the web. In this study, we propose an approach which we term "domain-adversarial and graph-attention neural network" (DAGA-NN) model to address the challenge. Its main advantage is that, in a text environment with multiple events/domains, only partial domain sample data are needed to train the model to achieve accurate cross-domain fake news detection in those domains with few (or even no) samples, which makes up for the limitations of traditional machine learning in fake news detection tasks due to news content evolution or cross-domain identification (where there is no sample data). Extensive experiments were conducted on two multimedia datasets of Twitter and Weibo, and the results showed that the proposed model was very effective in detecting fake news across events/domains.
- 文献类型：Article; Early Access
- 卷号：151
- ISSN号：0167-9236
- 是否译文：否