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Data Analysis and Knowledge Discovery
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A Fake News Detection Method Based On News Title-Content Difference
Liu Shang,Shen Yifan
(School of Science & Technology, Tianjin University of Finance and Economics, Tianjin 300222, China)
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Abstract  

[Objective] In order to solve the difficulties such as short news text and difficult to obtain comments, this paper proposes a fake news detection method based on the difference between news title and content. [Methods] Firstly, the Cos-Gap calculation method is designed to obtain the difference of textual and emotional features of news title and content. Then, according to the obtained differential features, based on the Heterogeneous Graph Attention Networks, this paper proposes a News Differential Heterogeneous Graph Network (NDHN). The network contains edges constructed based on differential features, and three types of nodes constructed based on semantic features and emotional features: title, content and emotion. [Results] The experimental results on the GossipCop dataset show the NDHN model detection method can improve the classification accuracy by 2.7%, and the F1 by 3.2%. [Limitations] This method is suitable for analyzing the news with title, has limitations for untitled texts such as microblog and Twitter. [Conclusions] The fusion of news differences can effectively improve the efficiency of fake news detection, and offer favorable help for social media to detect fake news quickly.

Key words Fake News Detection      Heterogeneous Graph Network      Differential Features      Public Opinion Analysis      
Published: 09 November 2022
ZTFLH:  TP391  

Cite this article:

Liu Shang, Shen Yifan. A Fake News Detection Method Based On News Title-Content Difference . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022-0293     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

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[2] Liu Shang, Shen Yifan. Detecting Fake News Based on Title-Content Difference[J]. 数据分析与知识发现, 2023, 7(2): 97-107.
[3] Liu Shuai, Fu Lifang. Identifying Fake News with External Knowledge and User Interaction Features[J]. 数据分析与知识发现, 2023, 7(11): 79-87.
[4] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[5] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[6] Wang Xiufang,Sheng Shu,Lu Yan. Analyzing Public Opinion from Microblog with Topic Clustering and Sentiment Intensity[J]. 数据分析与知识发现, 2018, 2(6): 37-47.
[7] Cen Yonghua,Wang Yuefen. Social Public Opinion Analysis and Decision Making Support with Big Data[J]. 现代图书情报技术, 2016, 32(7-8): 3-11.
[8] Duan Jianyong, Cheng Liwei, Zhang Mei, Gao Zhen'an. The Common Knowledge Mining for the Internet Public Opinion Analysis[J]. 现代图书情报技术, 2013, 29(10): 59-65.
[9] Wang Wei,Xu Xin. Online Public Opinion Hotspot Detection and Analysis Based on Document Clustering[J]. 现代图书情报技术, 2009, 3(3): 74-79.
[10] Qian Aibing. A Model for Analyzing Public Opinion Under the Web and Its Implementation[J]. 现代图书情报技术, 2008, 24(4): 49-55.
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