Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (3): 277-286.doi: 10.11896/jsjkx.240100204

• Artificial Intelligence • Previous Articles     Next Articles

Rumor Detection on Potential Hot Topics with Bi-directional Graph Attention Network

LI Shao, JIANG Fangting, YANG Xinyan, LIANG Gang   

  1. School of Cyber Science and Engineering,Sichuan University,Chengdu 610211,China
  • Received:2024-01-28 Revised:2024-05-15 Online:2025-03-15 Published:2025-03-07
  • About author:LI Shao,born in 1999,postgraduate.His main research interests include rumor detection and social network.
    LIANG Gang,born in 1976,Ph.D,associate professor,master supervisor.His main research interests include network security,online public opinion analysis and prediction,and AI security.
  • Supported by:
    National Natural Science Foundation of China(62162057).

PDF (PC)

212

Abstract: Most of the existing methods for detecting rumors on social media networks typically focus on individual posts as the target of detection,which leads to a cold start problem due to insufficient data,adversely affecting the detection performance.Moreover,these methods do not filter out the vast amount of irrelevant information in social media networks,resulting in longer detection latency and poorer performance.Additionally,current methods tend to emphasize static features during the spread of rumors when analyzing the characteristics of rumor propagation,making it difficult to fully leverage the dynamic relationships between nodes to model the complex propagation process.To address these issues,a rumor detection method based on potential hot topics and graph attention neural networks is proposed.The method employs a neural topic model and a potential hot topic discover model for topic-level rumor detection to overcome the cold start problem.Furthermore,a detection model named TPC-Bi-GAT is designed to analyze the dynamic features of rumor topic propagation for authenticity detection.Experiments on 3 public datasets show that the proposed method achieves a significant improvement of 3%~5% in accuracy compared with the existing methods,which verifies the effectiveness of the proposed method.

Key words: Rumor detection, Social network, Potential hot topic, Graph neural network, Topic cluster

CLC Number: 

  • TP181
[1]CAO J,GUO J B,LI X,et al.Automatic rumor detection on microblogs:A survey [J].arXiv:1807.03505,2018.
[2]GUPTA A,KUMARAGURU P,CASTILLO C,et al.Tweetcred:Real-time credibility assessment of content on twitter [C]//International Conference on Social Informatics.Springer,2014:228-243.
[3]MORRIS M R,COUNTS S,ROSEWAY A,et al.Tweeting is believing? Understanding microblog credibility perceptions [C]//Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work.2012:441-450.
[4]SUN S Y,LIU H Y,HE J,et al.Detecting event rumors on sina weibo automatically [C]//Asia-Pacific Web Conference.Sprin-ger,2013:120-131.
[5]LIU X M,NOURBAKHSH A,LI Q,et al.Realtime rumor debunking on twitter [C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.2015:1867-1870.
[6]JIN Z W,CAO J,ZHANG Y D,et al.Novel visual and statistical image features for microblogs news verification [J].IEEE Transactions on Multimedia,2016,19(3):598-608.
[7]MA J,GAO W,MITRA P,et al.Detecting rumors from microblogs with recurrent neural networks [C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:3818-3824.
[8]MA J,GAO W,WONG F K.Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning [C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).2017:708-717.
[9]BIAN T,XIAO X,XU T Y,et al.Rumor detection on social media with bi-directional graph convolutional networks [C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:549-556.
[10]HUANG Q,ZHOU C,WU J,et al.Deep spatial-temporal structure learning for rumor detection on Twitter[J].Neural Comput-ing and Applications,2023,35(18):12995-13005.
[11]LIN H Z,YI P Y,MA J,et al.Zero-shot rumor detection with propagation structure via prompt learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:5213-5221.
[12]LIU J W,XIE J Y,ZHANG F R,et al.Knowledge-EnhancedHierarchical Information Correlation Learning for Multi-Modal Rumor Detection[J].arXiv:2306.15946,2023.
[13]PI DE C,WU Z Y,CAO J.Early Rumor Detection MethodBased on Knowledge Graph Representation Learning[J].Acta Electonica Sinica,2023,51(2):385.
[14]TAN L,MA Z H,CAO J,et al.Rumor detection based on topic classification and multi-scale feature fusion[J/OL].https://iopscience.iop.org/article/10.1088/1742-6596/1601/3/032032/pdf.
[15]CASTILLO C,MENDOZA M,POBLETE B.Information credibility on twitter [C]//Proceedings of the 20th International Conference on World Wide Web.2011:675-684.
[16]YANG F,LIU Y,YU X H,et al.Automatic detection of rumor on sina weibo [C]//Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics.2012:1-7.
[17]KOWN S,CHA M Y,JUNG K,et al.Prominent features of rumor propagation in online social media [C]//IEEE 13th International Conference on Data Mining.IEEE,2013:1103-1108.
[18]ZUBIAGA A,AKER A,BONTCHEVA K,et al.Detection and resolution of rumours in social media:A survey [J].ACM Computing Surveys(CSUR),2018,51(2):1-36.
[19]WU Z Y,PI D C,CHEN J F,et al.Rumor detection based on propagation graph neural network with attention mechanism [J].Expert Systems with Applications,2020,158:113595.
[20]GUO H,CAO J,ZHANG Y Z,et al.Rumor detection with hierarchical social attention network [C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:943-951.
[21]AJAO O,BHOWMIK D,ZARGARI S.Fake news identification on twitter with hybrid cnn and rnn models [C]//Proceedings of the 9th International Conference on Social Media Snd society.2018:226-230.
[22]MA J,GAO W,WONG F K.Rumor detection on twitter with tree-structured recursive neural networks [C]//Association for Computational Linguistics.2018.
[23]HUANG Q,ZHOU C,WU J,et al.Deep structure learning for rumor detection on twitter [C]//2019 International Joint Conference on Neural Networks(IJCNN).IEEE,2019:1-8.
[24]GROOTENDORST M.BERTopic:Neural topic modeling with a class-based TF-IDF procedure [J].arXiv:2203.05794,2022.
[25]RONG Y,HUANG W B,XU T Y,et al.Dropedge:Towardsdeep graph convolutional networks on node classification [J].arXiv:1907.10903,2019.
[26]LIN H Z,MA J,CHEN M F,et al.Rumor detection on twitter with claim-guided hierarchical graph attention networks [J].arXiv:2110.04522,2021.
[27]ZENG F Z,GAO W.Early Rumor Detection Using NeuralHawkes Process with a New Benchmark Dataset [C]//Procee-dings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics.2022:4105-4117.
[28]MA J,GAO W,WEI Z Y,et al.Detect rumors using time series of social context information on microblogging websites [C]//The 24th ACM International on Conference on Information and Knowledge Management.2015:1751-1754.
[29]LI G H,DONG M,MING L F,et al.Deep reinforcement learning based ensemble model for rumor tracking [J].Information Systems,2022,103(C):101772.
[30]WU Z Y,PI D C,CHEN J F,et al.Rumor detection based on propagation graph neural network with attention mechanism [J].Expert Systems with Applications,2020,158:113595.
[31]THOTA R N,SUN X Y,DAI J.Early Rumor Detection in Social Media Based on Graph Convolutional Networks [C]//2023 International Conference on Computing,Networking and Communications(ICNC).IEEE,2023:516-522.
[32]YAO Y,ROSCSCO L,CAPOMMETTO A.On early stopping in gradient descent learning [J].Constructive Approximation,2007,26(2):289-315.
[1] YANG Yingxiu, CHEN Hongmei, ZHOU Lihua , XIAO Qing. Heterogeneous Graph Attention Network Based on Data Augmentation [J]. Computer Science, 2025, 52(3): 180-187.
[2] ZHENG Longhai, XIAO Bohuai, YAO Zewei, CHEN Xing, MO Yuchang. Graph Reinforcement Learning Based Multi-edge Cooperative Load Balancing Method [J]. Computer Science, 2025, 52(3): 338-348.
[3] HU Haifeng, ZHU Yiwen, ZHAO Haitao. Network Slicing End-to-end Latency Prediction Based on Heterogeneous Graph Neural Network [J]. Computer Science, 2025, 52(3): 349-358.
[4] YUAN Ye, CHEN Ming, WU Anbiao, WANG Yishu. Graph Anomaly Detection Model Based on Personalized PageRank and Contrastive Learning [J]. Computer Science, 2025, 52(2): 80-90.
[5] ZHENG Wenping, HAN Yiheng, LIU Meilin. Node Classification Algorithm Fusing High-order Group Structure Information [J]. Computer Science, 2025, 52(2): 107-115.
[6] WANG Xin, XIONG Shubo, SUN Lingyun. Federated Graph Learning:Problems,Methods and Challenges [J]. Computer Science, 2025, 52(1): 362-373.
[7] CHEN Liang, SUN Cong. Deep-learning Based DKOM Attack Detection for Linux System [J]. Computer Science, 2024, 51(9): 383-392.
[8] TANG Ying, WANG Baohui. Study on SSL/TLS Encrypted Malicious Traffic Detection Algorithm Based on Graph Neural Networks [J]. Computer Science, 2024, 51(9): 365-370.
[9] CHEN Shanshan, YAO Subin. Study on Recommendation Algorithms Based on Knowledge Graph and Neighbor PerceptionAttention Mechanism [J]. Computer Science, 2024, 51(8): 313-323.
[10] HU Haibo, YANG Dan, NIE Tiezheng, KOU Yue. Graph Contrastive Learning Incorporating Multi-influence and Preference for Social Recommendation [J]. Computer Science, 2024, 51(7): 146-155.
[11] LIU Wei, WU Fei, GUO Zhen, CHEN Ling. Two Stage Rumor Blocking Method Based on EHEM in Social Networks [J]. Computer Science, 2024, 51(7): 156-166.
[12] WEI Ziang, PENG Jian, HUANG Feihu, JU Shenggen. Text Classification Method Based on Multi Graph Convolution and Hierarchical Pooling [J]. Computer Science, 2024, 51(7): 303-309.
[13] DONG Wanqing, ZHAO Zirong, LIAO Huimin, XIAO Hui, ZHANG Xiaoliang. Research and Implementation of Urban Traffic Accident Risk Prediction in Dynamic Road Network [J]. Computer Science, 2024, 51(6A): 230500118-10.
[14] LI Shuai, YU Juan, WU Shaocheng. Cross-lingual Text Topic Discovery Based on Ensemble Learning [J]. Computer Science, 2024, 51(6A): 230300201-8.
[15] PENG Bo, LI Yaodong, GONG Xianfu, LI Hao. Method for Entity Relation Extraction Based on Heterogeneous Graph Neural Networks and TextSemantic Enhancement [J]. Computer Science, 2024, 51(6A): 230700071-5.
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.