Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11): 30-38.doi: 10.11896/jsjkx.240700004

• Social Media Fake News Detection • Previous Articles     Next Articles

Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection

YU Yongxin1,2, JI Ke1,2, GAO Yuan1,2, CHEN Zhenxiang1,2, MA Kun1,2, ZHAO Xiaofan3,4   

  1. 1 School of Information Science and Engineering,University of Jinan,Jinan 250022,China
    2 Shandong Provincial Key Laboratory of Network Based Intelligent Computing,Jinan 250022,China
    3 School of Information and Cyber Security,People’s Public Security University of China,Beijing 102623,China
    4 Key Laboratory of Security Prevention Technology and Risk Assessment of the Ministry of Public Security,Beijing 102623,China
  • Received:2024-07-01 Revised:2024-09-06 Online:2024-11-15 Published:2024-11-06
  • About author:YU Yongxin,born in 2000,postgra-duate,is a member of CCF(No.N4386G).Her main research interests include machine learning and natural language processing.
    JI Ke,born in 1989,Ph.D,associate professor,is a member of CCF(No.78936M).His main research interests include machine learning and recommendation systems.
  • Supported by:
    Shandong Provincial Key R & D Program of China(2021CXGC010103,2018CXGC0706) and Natural Science Foundation of Shandong Province,China(ZR2022LZH016).

PDF (PC)

340

Abstract: Social media platforms are inundated with a vast amount of unverified information,much of which originates from he-terogeneous data from multi-source,which spreads so widely and quickly that it poses a significant threat to individuals and society.Therefore,it is crucial to effectively detect and prevent fake news. Targeting the current limitations of fake news detection models,which typically rely on single data sources for news textual and visual information,resulting in strong subjective news reports and incomplete data coverage,a model is proposed for detecting fake news by progressively fusing multi-source heteroge-neous data.Firstly,multi-source heterogeneous data collection,screening,and cleaning are conducted to create a multi-source multimodal dataset containing reports about each event from diverse perspectives.Next,by inputting the features obtained from the textual feature extractor and visual feature extractor into the multi-source fusion module,a progressive fusion of features from various sources is achieved.Additionally,sentiment features extracted from text and frequency domain features extracted from images are incorporated into the model to enable multi-level feature extraction.Finally,this paper adopts the soft attention mechanism for feature integration.Experimental results and analysis show that the proposed model has better detection performance compared to existing popular methods,providing an effective solution for fake news detection in the era of big data.

Key words: Fake news detection, Data augmentation, Multi-source heterogeneous data, Feature fusion, Sentiment feature, Frequency domain feature

CLC Number: 

  • TP391
[1] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[J].Communication of the ACM,2017,60(6):84-90.
[2] ELMAN J L.Finding structure in time[J].Cognitive Science,1990,14(2):179-211.
[3] DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[4] RADFORD A,NARASIMHAN K,SALIMANS T,et al.Improving language understanding by generative pre-training[J/OL].https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
[5] KIM Y.Convolutional neural networks for sentence classification[J].arXiv:1408.5882,2014.
[6] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[7] LIU Z,LIN Y,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022.
[8] HUA J,CUI X,LI X,et al.Multimodal fake news detectionthrough data augmentation-based contrastive learning[J].Applied Soft Computing,2023,136:110125.
[9] MAHAJAN S,ROTH S.Diverse image captioning with context-object split latent spaces[J].Advances in Neural Information Processing Systems,2020,33:3613-3624.
[10] LI Y,JI K,MA K,et al.Fake News Detection Based on the Correlation Extension of Multimodal Information[C]//Asia-Pacific Web(APWeb) and Web-Age Information Management(WAIM) Joint International Conference on Web and Big Data.Cham:Springer Nature Switzerland,2022:443-450.
[11] SU J.Simbert:Integrating retrieval and generation into bert[J/OL].Tech.Rep.https://github.com/Zhuiyi Technology/simbert.
[12] MA J,GAO W,MITRA P,et al.Detecting rumors from micro-blogs with recurrent neural networks[C]//Proceedings of IJCAI.2016:3818-3824.
[13] YU F,LIU Q,WU S,et al.A Convolutional Approach for Misinformation Identification[C]//IJCAI.2017:3901-3907.
[14] MA J,GAO W,WONG K F.Detect rumor and stance jointly by neural multi-task learning[C]//Companion Proceedings of the Web Conference 2018.2018:585-593.
[15] VAIBHAV V,ANNASAMMY R M,HOVY E.Do sentence interactions matter? leveraging sentence level representations for fake news classification[J].arXiv:1910.12203,2019.
[16] JIAN W,LI J P,AKBAR A,et al.SA-Bi-LSTM:Self Attention With Bi-Directional LSTM based Intelligent Model for Accurate Fake News Detection to ensured information integrity on social media platforms[J].IEEE Access,2024,12:48436-48452.
[17] SUN L,WANG H.Topic-Aware Fake News Detection Based on Heterogeneous Graph[J].IEEE Access,2023,11:103743-103752.
[18] QI P,CAO J,YANG T,et al.Exploiting multi-domain visual information for fake news detection[C]//2019 IEEE International Conference on Data Mining(ICDM).IEEE,2019:518-527.
[19] WANG Y,MA F,JIN Z,et al.Eann:Event adversarial neural networks for multi-modal fake news detection[C]//Proceedings of the 24th ACM Sigkdd International Conference on Knowledge Discovery & Data Mining.2018:849-857.
[20] KHATTAR D,GOUD J S,GUPTA M,et al.Mvae:Multimodal variational autoencoder for fake news detection[C]//The World Wide Web Conference.2019:2915-2921.
[21] ZHOU X,WU J,ZAFARANI R.Safe:Similarity-aware multi-modal fake news detection.arxiv[J].arXiv:2003.04981,2020.
[22] WU Y,ZHAN P,ZHANG Y,et al.Multimodal fusion with co-attention networks for fake news detection[C]//Findings of the Association for Computational Linguistics:ACL-IJCNLP 2021.2021:2560-2569.
[23] QIAN S,WANG J,HU J,et al.Hierarchical multi-modal contextual attention network for fake news detection[C]//Procee-dings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:153-162.
[24] GUO Y,GE H,LI J.A two-branch multimodal fake news detection model based on multimodal bilinear pooling and attention mechanism[J].Frontiers in Computer Science,2023,5:1159063.
[25] JING J,WU H,SUN J,et al.Multimodal fake news detection via progressive fusion networks[J].Information Processing & Management,2023,60(1):103120.
[26] GUO Y.A mutual attention based multimodal fusion for fake news detection on social network[J].Applied Intelligence,2023,53(12):15311-15320.
[27] LIU Y,BING W,REN S,et al.BC-FND:An Approach Based on Hierarchical Bilinear Fusion and Multimodal Consistency for Fake News Detection[J].IEEE Access,2024,12:62738-62749.
[28] PENG L,JIAN S,KAN Z,et al.Not all fake news is semantically similar:Contextual semantic representation learning for multi-modal fake news detection[J].Information Processing & Ma-nagement,2024,61(1):103564.
[29] WANG J,ZHENG J,YAO S,et al.TLFND:A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection[J].Entropy,2023,25(11):1533.
[30] MIHALCEA R,TARAU P.Textrank:Bringing order into text[C]//Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing.2004:404-411.
[31] YU Z,YU J,FAN J,et al.Multi-modal factorized bilinear pooling with co-attention learning for visual question answering[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:1821-1830.
[32] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[33] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[34] JIN Z,CAO J,GUO H,et al.Multimodal fusion with recurrent neural networks for rumor detection on microblogs[C]//Proceedings of the 25th ACM International Conference on Multimedia.2017:795-816.
[1] LI Xin, PU Yuanyuan, ZHAO Zhengpeng, LI Yupan, XU Dan. Image Arbitrary Style Transfer via Artistic Aesthetic Enhancement [J]. Computer Science, 2024, 51(9): 129-139.
[2] DAI Chaofan, DING Huahua. Domain-adaptive Entity Resolution Algorithm Based on Semi-supervised Learning [J]. Computer Science, 2024, 51(9): 214-222.
[3] LIU Qian, BAI Zhihao, CHENG Chunling, GUI Yaocheng. Image-Text Sentiment Classification Model Based on Multi-scale Cross-modal Feature Fusion [J]. Computer Science, 2024, 51(9): 258-264.
[4] LIU Sichun, WANG Xiaoping, PEI Xilong, LUO Hangyu. Scene Segmentation Model Based on Dual Learning [J]. Computer Science, 2024, 51(8): 133-142.
[5] WANG Chao, TANG Chao, WANG Wenjian, ZHANG Jing. Infrared Human Action Recognition Method Based on Multimodal Attention Network [J]. Computer Science, 2024, 51(8): 232-241.
[6] CAI Wenliang, HUANG Jun. Lane Detection Method Based on RepVGG [J]. Computer Science, 2024, 51(7): 236-243.
[7] LI Guo, CHEN Chen, YANG Jing, QUN Nuo. Study on Tibetan Short Text Classification Based on DAN and FastText [J]. Computer Science, 2024, 51(6A): 230700064-5.
[8] YIN Xudong, CHEN Junyang, ZHOU Bo. Study on Industrial Defect Augmentation Data Filtering Based on OOD Scores [J]. Computer Science, 2024, 51(6A): 230700111-7.
[9] KANG Zhiyong, LI Bicheng, LIN Huang. User Interest Recognition Method Incorporating Category Labels and Topic Information [J]. Computer Science, 2024, 51(6A): 230500169-8.
[10] HAN Zhigeng, ZHOU Ting, CHEN Geng, FU Chunshuo, CHEN Jian. RM-RT2NI:A Recommendation Model with Review Timeliness and Trusted Neighbor Influence [J]. Computer Science, 2024, 51(6A): 230800160-7.
[11] WANG Yanlin, SUN Jing, YANG Hongbo, GUO Tao, PAN Jiahua, WANG Weilian. Classification Model of Heart Sounds in Pulmonary Hypertension Based on Time-Frequency Fusion Features [J]. Computer Science, 2024, 51(6A): 230800091-7.
[12] QUE Yue, GAN Menghan, LIU Zhiwei. Object Detection with Receptive Field Expansion and Multi-branch Aggregation [J]. Computer Science, 2024, 51(6A): 230600151-6.
[13] LIU Heng, LIN Hongyu, WU Tao. Detection Method for Workers’ Illegal Operation Behavior in PackagingWorkshop of CigaretteFactory [J]. Computer Science, 2024, 51(6A): 230700123-8.
[14] LI Yuehao, WANG Dengjiang, JIAN Haifang, WANG Hongchang, CHENG Qinghua. LiDAR-Radar Fusion Object Detection Algorithm Based on BEV Occupancy Prediction [J]. Computer Science, 2024, 51(6): 215-222.
[15] HUO Xingxing, HU Ruimin, LI Yixin. Early-stage Fatigue Detection Based on Frequency Domain Information of Eye Features [J]. Computer Science, 2024, 51(6): 247-255.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.