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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (2): 97-107    DOI: 10.11925/infotech.2096-3467.2022.0293
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Detecting Fake News Based on 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] This paper proposes a fake news detection method based on the difference between news titles and contents, aiming to address the issues of extracting features from short news texts or retrieving comments. [Methods] Firstly, we designed the Cos-Gap calculation method to obtain the difference between news titles and contents’ textual and emotional features. Then, we constructed a News Differential Heterogeneous Graph Network (NDHN) based on the obtained differential features and the Heterogeneous Graph Attention Networks. The NDHN contains edges constructed based on differential features and nodes constructed based on semantic and emotional features of title, content, and emotion. [Results] We examined the proposed model on the GossipCop dataset and found that the NDHN 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 and has limitations for untitled texts from Sina Weibo or Twitter. [Conclusions] The new model could effectively detect fake news from social media.
Key words： Fake News Detection      Heterogeneous Graph Network      Differential Features      Public Opinion Analysis     
Received: 02 April 2022      Published: 28 March 2023
ZTFLH:  TP391  
Fund:Humanities and Social Sciences Research Planning Project of the Ministry of Education(19YJA630046);Natural Science Foundation of Tianjin(20JCQNJC00970);Tianjin Art Science Planning Project(C22030)
Corresponding Authors: Liu Shang，ORCID：0000-0002-3797-7339，E-mail： liushangw@tjufe.edu.cn。   
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Shang Liu
Yifan Shen

Cite this article:

Liu Shang, Shen Yifan. Detecting Fake News Based on Title-Content Difference. Data Analysis and Knowledge Discovery, 2023, 7(2): 97-107.

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/Y2023/V7/I2/97

Structure of NDHN
A t i t l e - c o n t e n t
">
Title-Content Adjacency Matrix A t i t l e - c o n t e n t
A e m o t i o n - t i t l e
">
Emotion-Title Adjacency Matrix A e m o t i o n - t i t l e
A e m o t i o n - c o n t e n t
">
Emotion-Content Adjacency Matrix A e m o t i o n - c o n t e n t
Adjacency Matrix of the NDHN Network
数据集 指标 SVM RFC DTC GRU-2 B-TransE KAN NDHN
GossipCop 准确率 0.664 3 0.691 8 0.695 9 0.718 0 0.739 4 0.776 6 0.803 9
F1 0.595 5 0.669 1 0.691 9 0.707 9 0.734 0 0.771 3 0.803 7
Results of Different Model Experiments
方法 准确率 F1
-Word Similarity 0.789 5 0.789 4
-Attention 0.800 0 0.799 1
-Emotion Gap 0.801 3 0.800 3
NDHN 0.803 9 0.803 7
Experimental Results of Ablation of NDHN
The Number of News Title-Content Differences Under Different Emotion Labels
Histogram of News Title-Content Text Difference Frequency
Histogram of News Title-Content Emotion Difference Frequency
[1] Vosoughi S, Roy D, Aral S. The Spread of True and False News Online[J]. Science, 2018, 359(6380): 1146-1151.
doi: 10.1126/science.aap9559 pmid: 29590045
[2] Mian A, Khan S. Coronavirus: The Spread of Misinformation[J]. BMC Medicine, 2020, 18(1): Article No.89.
[3] Kwak H, Lee C, Park H, et al. What is Twitter, a Social Network or a News Media?[C]// Proceedings of the 19th International Conference on World Wide Web. 2010: 591-600.
[4] Gabielkov M, Ramachandran A, Chaintreau A, et al. Social Clicks: What and Who Gets Read on Twitter?[C]// Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science. 2016: 179-192.
[5] Ecker U K H, Lewandowsky S, Chang E P, et al. The Effects of Subtle Misinformation in News Headlines[J]. Journal of Experimental Psychology: Applied, 2014, 20(4): 323-335.
doi: 10.1037/xap0000028
[6] Horne B, Adali S. This JustIn:Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire Than Real News[C]// Proceedings of the 2nd International Workshop on News and Public Opinion at ICWSM. 2017.
[7] Hu L M, Yang T C, Shi C, et al. Heterogeneous Graph Attention Networks for Semi-Supervised Short Text Classification[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 4823-4832.
[8] Castillo C, Mendoza M, Poblete B. Information Credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. ACM, 2011: 675-684.
[9] Shu K, Sliva A, Wang S, et al. Fake News Detection on Social Media: A Data Mining Perspective[C]// Proceedings of the 2017 ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2017: 22-36.
[10] Ma J, Gao W, Mitra P, et al. Detecting Rumors from Microblogs with Recurrent Neural Networks[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. ACM, 2016: 3818-3824.
[11] Potthast M, Kiesel J, Reinartz K, et al. A Stylometric Inquiry into Hyperpartisan and Fake News[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 231-240.
[12] Shu K, Cui L M, Wang S H, et al. dEFEND: Explainable Fake News Detection[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 395-405.
[13] Rashkin H, Choi E, Jang J Y, et al. Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 2931-2937.
[14] Liu Y, Wu Y F. Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks[C]// Proceedings of the 2018 AAAI Conference on Artificial Intelligence. 2018: 254-261.
[15] Shrestha A, Spezzano F. Textual Characteristics of News Title and Body to Detect Fake News: A Reproducibility Study[C]// Proceedings of the 2021 European Conference on Information Retrieval. 2021: 120-133.
[16] Ajao O, Bhowmik D, Zargari S. Sentiment Aware Fake News Detection on Online Social Networks[C]// Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing. 2019: 2507-2511.
[17] Giachanou A, Rosso P, Crestani F. Leveraging Emotional Signals for Credibility Detection[C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019: 877-880.
[18] Wu L W, Rao Y. Adaptive Interaction Fusion Networks for Fake News Detection[C]// Proceedings of the 24th European Conference on Artificial Intelligence. 2020: 2220-2227.
[19] Zhang X Y, Cao J, Li X R, et al. Mining Dual Emotion for Fake News Detection[C]// Proceedings of the 2021 International Conference on World Wide Web. ACM, 2021: 3465-3476.
[20] Ghanem B, Rosso P, Rangel F. An Emotional Analysis of False Information in Social Media and News Articles[J]. ACM Transactions on Internet Technology, 2020, 20(2): Article No.19.
[21] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2019: 4171-4186.
[22] Kant N, Puri R, Yakovenko N, et al. Practical Text Classification with Large Pre-Trained Language Models[OL]. arXiv Preprint, arXiv: 1812.01207.
[23] Kipf T N, Welling M.Semi-Supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv: 1609.02907.
[24] Shu K, Mahudeswaran D, Wang S H, et al. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media[J]. Big Data, 2020, 8(3): 171-188.
doi: 10.1089/big.2020.0062
[25] Yang F, Liu Y, Yu X H, et al. Automatic Detection of Rumor on Sina Weibo[C]// Proceedings of the 2012 ACM SIGKDD Workshop on Mining Data Semantics. 2012:Article No.13.
[26] Kwon S, Cha M, Jung K, et al. Prominent Features of Rumor Propagation in Online Social Media[C]// Proceedings of the IEEE 13th International Conference on Data Mining. 2013: 1103-1108.
[27] Pan J Z, Pavlova S, Li C, et al. Content Based Fake News Detection Using Knowledge Graphs[C]// Proceedings of the 17th International Semantic Web Conference. 2018: 669-683.
[28] Dun Y, Tu K, Chen C, et al. KAN: Knowledge-Aware Attention Network for Fake News Detection[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021: 81-89.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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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