外部知識(shí)與內(nèi)部上下文語義聚合的短文本新聞虛假檢測(cè)模型

打開文本圖片集

中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A

Short Text News Fake Detection Model Based on Aggregating External Knowledge and Internal Contextual Semantics

QIU Yanfang 1 ， ZHAO Zhenyu 2 ， SUN Zhijie'， MA Kun’ ， JI Ke1 ， CHEN Zhenxiang 1

（1.a.School of Information Science and Engineering，b.Shandong KeyLaboratory of Ubiquitous Inteligent Computing， University of Jinan，Jinan 250O22，Shandong，China; 2. Shandong Talent Development Group Information Technology Co.， Ltd.， Jinan ， Shandong，China）

Abstract：To adressthe problem ofsemantic feature sparsityin shorttext news and the neglectof the homology between external knowledge and thesemanticsof short-text news，ashort text news fake detection model basedonagregating external knowledge and internal contextual semantics （EKCS-ST）was proposed.A news feature information network was constructed，which included three typesof external knowledge，such as news topics，authors，and entities，to enrichthe semantic featuresof short text news.The exteral knowledge graph features of the news were generated through graph convolution.The newstext was fed intoa text encoder to capture internal contextual semantic features.These external knowledgegraph featuresand internal contextual semantic features were thenused in a context-aware computation to strengthen thecorrelation between external knowledgeand contextual semantics.Theatention mechanism wasutilized to selectand enhance the keyfeaturesof the news，whiletheloss errorfor minority-classnews was increased to mitigate the data imbalance issue.The results show that F1 score of the proposed model，which is the harmonic mean of precision and recall，is O.86，outperforming BERT and TextGCN models by 18% and 17% ，respectively，validating the effectiveness of the model.

Keywords： short text news fake detection；external knowledge；attention mechanism；semantic feature

自媒體發(fā)布了大量快訊、頭條等表達(dá)簡(jiǎn)短扼要的短文本新聞，未經(jīng)鑒別的新聞?wù)鎸?shí)性無法保證[1]。（剩余13876字）