重庆理工大学学报2024，Vol.38Issue(19)：122-131,10.DOI:10.3969/j.issn.1674-8425(z).2024.10.015
情感感知增强的多粒度过滤虚假新闻检测
Emotion-aware enhanced multi-granularity filter fake news detection
摘要
Abstract
Evidence-based fake news detection is a challenging task that requires retrieving multiple pieces of evidence from the Internet to verify the authenticity of the news.Although the current methods achieve fairly good performances,there are still some problems.For example,they fail to consider the negative impacts of irrelevant evidence retrieved from the Internet on the model,and the processing of noise information in evidence text needs improving.Moreover,they ignore the impact of emotional polarity of news on the authenticity of news.To address these problems,this paper proposes an emotion-aware enhanced multi-granularity filter fake news detection,called EMGFND.First,the text information of news and evidence is aggregated through the graph structure modeling.Fine evidence information is then obtained through multi-granularity filtering.Finally,the news and evidence are interacted through the news emotion-aware attention mechanism.Several experiments are conducted on two public datasets(Snopes and PolitiFact).Our experimental results show the proposed model performs better than the baseline model.关键词
虚假新闻检测/图神经网络/情感分析/文本分类/图结构学习Key words
fake news detection/graph neural network/sentiment analysis/text classification/graph structure learning分类
信息技术与安全科学引用本文复制引用
李潇可,朱小飞..情感感知增强的多粒度过滤虚假新闻检测[J].重庆理工大学学报,2024,38(19):122-131,10.基金项目
国家自然科学基金项目(62141201) （62141201）
重庆市自然科学基金项目(CSTB2022NSCQ-MSX1672) （CSTB2022NSCQ-MSX1672）
重庆市教育委员会科学技术研究计划重大项目(KJZD-M202201102) （KJZD-M202201102）
