基于混合深度模型的虚假信息早期检测

doi:10.6040/j.issn.1672-3961.0.2021.296

摘要/Abstract

摘要： 针对一种信息特征进行检测方法在信息传播早期阶段提取的特征信息往往不充分,导致传播早期阶段检测准确率较低的问题,提出一个新颖的混合深度模型EGSI,模型由EXTRACT、GRU、SCORE和INTERATE 4个模块组成。EXTRACT通过卷积神经网络提取信息的传播路径特征,GRU通过门控循环单元捕获信息的文本特征和反馈特征,SCORE基于用户行为挖掘用户特征,INRERATE整合以上特征并预测出信息事件类标。EGSI通过整合信息最基本的4种特征(文本、用户、反馈、传播路径),从而可以在信息传播的早期阶段充分提取可用特征信息,进而较准确地检测出虚假信息。真实数据集的试验结果表明,模型在信息传播60 min内的准确率达到95.9%。相比基准方法,EGSI模型在检测虚假信息的准确率和时效性之间取得了较好的平衡。

关键词: 虚假信息早期检测, 混合深度模型, 神经网络, 时序分析, 信息特征

中图分类号:

TP181

黄皓,周丽华,黄亚群,姜懿庭. 基于混合深度模型的虚假信息早期检测[J]. 山东大学学报 (工学版), 2022, 52(4): 89-98.

HUANG Hao, ZHOU Lihua, HUANG Yaqun, JIANG Yiting. Early detection of fake news based on hybrid deep model[J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 89-98.

参考文献

[1] CASTILLO C, MENDOZA M,POBLETE B. Information credibility on twitter[C] //Proceedings of the 20th International Conference on World Wide Web. Hyderabad, India: ACM, 2011: 675-684.
[2] QAZVINIAN V, ROSENGREN E, RADEV D R, et al. Rumor has it: identifying misinformation in microblogs[C] //Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, UK: EMNLP, 2011: 1589-1599.
[3] GUPTA A, KUMARAGURU P, CASTILLO C, et al. TweetCred: real-time credibility assessment of content on twitter[C] //Proceedings of the International Conference on Social Informatics. Barcelona, Spain: SocInfo, 2014: 228-243.
[4] POPAT K. Assessing the credibility of claims on the Web[C] //Proceedings of the 26th International Conference on World Wide Web. Perth, Australia: International World Wide Web Conferences Steering Committee, 2017: 735-739.
[5] YANG F, YU X, LIU Y, et al. Automatic detection of rumor on Sina Weibo[C] //Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. Sydney, Australia: ACM, 2015: 1-7.
[6] ZHAO Z, RESNICK P, MEI Q. Enquiring minds: early detection of rumors in social media from enquiry posts[C] //Proceedings of the 24th International Conference on World Wide Web. Geneva,Switzerland:International World Wide Web Conferences Steering Committee, 2015: 1395-1405.
[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. New York, USA: AAAI Press, 2016: 3818-3824.
[8] MA J, GAO W, WONG K F. Detect rumors in microblog posts using propagation structure via kernel learning[C] //Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: IJCAI, 2017: 708-717.
[9] JIN F, DOUGHERTY E R, SARAF P, et al. Epidemiological modeling of news and rumors on Twitter[C] //Proceedings of the 7th Workshop on Social Network Mining and Analysis. Chicago, USA: ACM, 2013: 1-9.
[10] WU K, YANG S, ZHU K Q. False rumors detection on Sina Weibo by propagation structures[C] //Proceedings of the 31st IEEE International Conference on Data Engineering. Seoul, Korea: ICDEW, 2015: 651-662.
[11] CHEN T, LI X, YIN H, et al. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection[C] // Proceedings of Trends and Applications in Knowledge Discovery and Data Mining. Melbourne, Australia: LNCS, 2018: 40-52.
[12] LI Q, HU Q, LU Y, et al. Multi-level word features based on CNN for fake news detection in cultural communication[J]. Personal and Ubiquitous Computing, 2020, 24(2): 259-272.
[13] CHOWDHURY R, SRINIVASAN S, GETOOR L. Joint estimation of user and publisher credibility for fake news detection[C] //Proceedings of the 29th ACM Intern-ational Conference on Information & Knowledge Management. New York, America: CIKM, 2020: 1993-1996.
[14] BALESTRUCCI A, NICOLA R D. Credulous users and fake news: a real case study on the propagation in Twitter[C] //Proceedings of 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems(EAIS). Bari, Italy: IEEE, 2020: 1-8.
[15] HAMDI T, SLIMI H, BOUNHAS I, et al. A hybrid approach for fake news detection in Twitter based on user features and graphembedding[C] //Proceedings of Distributed Computing and Internet Technology. Bhubaneswar, India: ICDCIT, 2020: 266-280
[16] JIANG S, CHEN X, ZHANG L, et al. User-characteristic enhanced model for fake news detection in social media[C] //Proceedings of Natural Language Processing and Chinese Computing. Dunhuang, China: NLPCC, 2019: 634-646.
[17] SAMPSON J, MORSTATTER F, WU L, et al. Leveraging the implicit structure within social media for emergent rumor detection[C] //Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Turin, Italy: CIKM, 2016: 2377-2382.
[18] 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 AAAI Conference on Artificial Intelligence. Louisiana, USA: AAAI, 2018: 354-361.
[19] QIAN F, GONG C, SHARMA K, et al. Neuraluser response generator: fake news detection with collective user intelligence[C] //Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Washington, USA: IJCAI, 2018: 3834-3840.
[20] CASTILLO C, EL-HADDAD M, PFEFFER J, et al. Characterizing the life cycle of online news stories using social media reactions[C] //Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. Baltimore, Maryland: CSCW, 2014: 211-223.
[21] FRIGGERI A, ADAMIC L A, ECKLES D, et al. Rumor cascades[C] //Proceedings of International AAAI Conference on Web and Social Media. Ann Arbor, USA: ICWSM, 2014: 1-13.
[22] KUMAR S, WEST R, LESKOVEC J. Disinformation on the web: impact, characteristics, and detection of wikipediahoaxes[C] //Proceedings of the 25th International Conference on World Wide Web. Quebec, Canada: International World Wide Web Conferences Steering Committee, 2016: 591-602.
[23] STARBIRD K, MADDOCK J, ORAND M, et al. Rumors, false flags, and digital vigilantes: misinformation on Twitter after the 2013 Boston Marathon Bombing[C] //Proceedings of the IConference 2014. Illinois, America: ISchool, 2014: 654-662.
[24] KWON S, CHA M, JUNG K. Rumor detection over varying time windows[J]. PLOS ONE, 2017,12(1):1-19.
[25] RUCHANSKY N, SEO S, LIU Y. CSI: ahybrid deep model for fake news detection[C] //Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: CIKM, 2017: 797-806.

相关文章 15

[1]	尹旭,刘兆英,张婷,李玉鑑. 基于弱监督和半监督学习的红外舰船分割方法[J]. 山东大学学报 (工学版), 2022, 52(2): 99-106.
[2]	王心哲,邓棋文,王际潮,范剑超. 深度语义分割MRF模型的海洋筏式养殖信息提取[J]. 山东大学学报 (工学版), 2022, 52(2): 89-98.
[3]	张学思,张婷,刘兆英,江天鹏. 基于轻量型卷积神经网络的海面红外显著性目标检测方法[J]. 山东大学学报 (工学版), 2022, 52(2): 41-49.
[4]	丁飞,江铭炎. 基于改进狮群算法和BP神经网络模型的房价预测[J]. 山东大学学报 (工学版), 2021, 51(4): 8-16.
[5]	尹晓敏,孟祥剑,侯昆明,陈亚潇,高峰. 一种计及空间相关性的光伏电站历史出力数据的修正方法[J]. 山东大学学报 (工学版), 2021, 51(4): 118-123.
[6]	陶亮,刘宝宁,梁玮. 基于CNN-LSTM 混合模型的心律失常自动检测[J]. 山东大学学报 (工学版), 2021, 51(3): 30-36.
[7]	廖锦萍,莫毓昌,YAN Ke. 基于C-LSTM的短期用电预测模型和应用[J]. 山东大学学报 (工学版), 2021, 51(2): 90-97.
[8]	霍兵强,周涛,陆惠玲,董雅丽,刘珊. 基于NRC和多模态残差神经网络的肺部肿瘤良恶性分类[J]. 山东大学学报 (工学版), 2020, 50(6): 59-67.
[9]	孙东磊,王艳,于一潇,韩学山,杨明,闫芳晴. 基于BP神经网络的短期光伏集群功率区间预测[J]. 山东大学学报 (工学版), 2020, 50(5): 70-76.
[10]	王志伟,葛楠,李春伟. 基于BP神经网络算法的结构振动模态模糊控制[J]. 山东大学学报 (工学版), 2020, 50(5): 13-19.
[11]	廖南星,周世斌,张国鹏,程德强. 基于类激活映射-注意力机制的图像描述方法[J]. 山东大学学报 (工学版), 2020, 50(4): 28-34.
[12]	彭岩,冯婷婷,王洁. 基于集成学习的O₃的质量浓度预测模型[J]. 山东大学学报 (工学版), 2020, 50(4): 1-7.
[13]	蔡国永,贺歆灏,储阳阳. 基于空间注意力和卷积神经网络的视觉情感分析[J]. 山东大学学报 (工学版), 2020, 50(4): 8-13.
[14]	李怡霏,郭尊华. 一种Chirplet神经网络自动目标识别算法[J]. 山东大学学报 (工学版), 2020, 50(3): 8-14.
[15]	金保明,卢光毅,王伟,杜伦阅. 基于弹性梯度下降算法的BP神经网络降雨径流预报模型[J]. 山东大学学报 (工学版), 2020, 50(3): 117-124.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed