计算机应用与软件2024，Vol.41Issue(8)：189-195,7.DOI:10.3969/j.issn.1000-386x.2024.08.027
基于贴文级特征融合的社交网络谣言检测方法
SOCIAL NETWORKS RUMOR DETECTION APPROACH BASED ON POST-LEVEL FEATURE FUSION
摘要
Abstract
The existing rumor detection methods largely neglect the correlation between post semantics,post publishers and post propagation status,which lead to low detection rates.To solve this problem,this paper proposes a rumor detection approach PF-HAN based on post-level feature fusion.The model used a Bi-LSTM with attention mechanism to generate the semantic representation of each post,and spliced it with the social network features of the corresponding post to preserve the correspondence between them.The integrated representation of the posts obtained by the fusion was input into the hierarchical attention network in the form of sequence to extract the temporal features and generate the final event representation for rumor discrimination.Experimental results over Sina Weibo and Twitter show that the Fl value of the model reaches 0.956 and 0.740 when the model performs the rumor detection task and it can complete the early rumor detection task with high accuracy.关键词
深度学习/自然语言处理/谣言检测/特征融合Key words
Deep learning/Natural language processing/Rumor detection/Feature fusion分类
信息技术与安全科学引用本文复制引用
余潇龙,郭天成,陈阳,王新..基于贴文级特征融合的社交网络谣言检测方法[J].计算机应用与软件,2024,41(8):189-195,7.基金项目
上海市自然科学基金项目(16ZR1402200). （16ZR1402200）
