计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 97-112.doi: 10.11896/jsjkx.210200023

所属专题: 多媒体技术进展

• 多媒体技术进展* 上一篇    下一篇

多媒体社会事件分析综述

钱胜胜1, 张天柱2, 徐常胜1   

  1. 1 中国科学院自动化研究所 北京100190
    2 中国科学技术大学信息科学技术学院 合肥230026
  • 收稿日期:2021-01-16 修回日期:2021-02-01 出版日期:2021-03-15 发布日期:2021-03-05
  • 通讯作者: 徐常胜(csxu@nlpr.ia.ac.cn)
  • 作者简介:shengsheng.qian@nlpr.ia.ac.cn
  • 基金资助:
    国家自然科学基金(61802405, 61751211)

Survey of Multimedia Social Events Analysis

QIAN Sheng-sheng1, ZHANG Tian-zhu2, XU Chang-sheng1   

  1. 1 Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
    2 School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China
  • Received:2021-01-16 Revised:2021-02-01 Online:2021-03-15 Published:2021-03-05
  • About author:QIAN Sheng-sheng,born in 1991,Ph.D,associate professor.His main research interests include social media data mi-ning and multimedia analysis.
    XU Chang-sheng,born in 1969,Ph.D,professor.His main research interests include computer vision and multimedia analysis.
  • Supported by:
    National Natural Science Foundation of China (61802405, 61751211).

摘要: 由于网络技术的飞速发展,自媒体、微博、论坛等基于互联网的多种交流渠道日渐完善,人们能够方便地在线生成和共享丰富的社会多媒体内容。社会事件数据具有跨平台、多模态、大规模、噪声大等特点,基于多媒体社会事件的分析研究非常具有挑战性。因此,如何对社会媒体数据进行处理,研究社会事件分析方法、设计有效的社会事件分析模型成为社会事件分析研究的关键问题。文中对近年来多媒体社会事件分析的相关研究展开了综述,重点回顾了多媒体社会事件表示方法及其在虚假新闻检测、多媒体热点事件检测跟踪及演化分析、社交媒体危机事件响应等领域的应用,并对不同应用涉及的数据集进行了详细介绍。最后对多媒体社会事件分析方面未来可能的研究课题进行了展望。

关键词: 表示学习, 多媒体, 多模态, 社会事件, 深度学习

Abstract: With the rapid development of network technology,various Internet-based communication channels,such as self-media,Weibo,BBS,are becoming perfect platforms for people to easily generate and share rich social multimedia content online.Social event data have the characteristics of multi-platform,multi-modal,large-scale and high noise,which bring huge challenges for the analysis and research based on multimedia social events.Therefore,how to process social media data,study social event analysis methods,and design effective social event analysis models become key issues in social event analysis research.This paper presents a review of relevant research in multimedia social event analysis in recent years,focusing on multimedia social event representation methods and their applications in the fields of fake news detection,multimedia hot event detection,tracking and evolution analysis,as well as social media crisis event response.In addition,the datasets involved in different applications are introduced in detail.In the last section,this paper discusses possible future research topics in multimedia social event analysis.

Key words: Deep learning, Multimedia, Multimodal, Representation learning, Social event

中图分类号: 

  • TP391
[1]ATEFEH F,KHREICH W.A Survey of Techniques for Event Detection in Twitter [J].Computational Intelligence,2015,31(1):132-164.
[2]GARG M,KUMAR M.Review on Event Detection Techniques in Social Multimedia [J].Online Information Review,2016,40(3):347-361.
[3]ZEPPELZAUER M,SCHOPFHAUSER D.Multimodal Classification of Events in Social Media [J].Image and Vision Computing,2016,53(SEP.):45-56.
[4]ZHOU H K,YU H M.A Survey on Trends of Cross-MediaTopic Evolution Map [J].KNOWL-BASED SYST,2017,124,164-175.
[5]LIU X L,WANG M,HUET B.Event Analysis in Social Multimedia:A Survey [J].Frontiers Comput.Sci,2016,10(3):433-446.
[6]QIAN S S,ZHANG T Z,XU C S.A Research and Prospect ofMultimedia Social Event Analysis [J].Journal of Nanjing University of Information Science & Technology:Natural Science Edition,2017,9(6):599-612.
[7]ZHOU H,YIN H,ZHENG H,et al.A Survey on Multi-Modal Social Event Detection [J].Knowledge-Based Systems,2020,195:105695.
[8]DEBOLE F,SEBASTIANI F.Supervised Term Weighting forAutomated Text Categorization[C]//Proceedings of the 2003 ACM Symposium on Applied Computing.2003:784-788.
[9]JONES K S.A Statistical Interpretation of Term Specificity and its Application in Retrieval [J].Journal of Documentation,2004,60(5):493-502.
[10]DEERWESTER S,DUMAIS S T,FURNAS G W,et al.Indexing by Latent Semantic Analysis[J].Journal of the Association for Information Science & Technology,2010,41(6):391-407.
[11]HOFMANN T.Probabilistic Latent Semantic Indexing[C]//Proceedings of the international Acm Sigir Conference on Research & Development in Information Retrieval.ACM,1999.
[12]BLEI D M,NG A,JORDAN M I.Latent Dirichlet Allocation[J].The Journal of Machine Learning Research,2003,3(4/5):993-1022.
[13]BLEI D M,MCAULIFFE J D.Supervised Topic Models[J].Advances in Neural Information Processing Systems,2010,3:327-332.
[14]HINTON G E,SALAKHUTDINOV R R.Supporting OnlineMaterial for “Reducing the Dimensionality of Data with Neural Networks”[J/OL].Methods,2006,504.http://www.utstat.utoronto.ca/~rsalakhu/papers/science_som.pdf.
[15]HINTON G E,OSINDERO S,TEH Y W.A Fast Learning Algorithm for Deep Belief Nets [J].Neural Computation,2006,18(7):1527-1554.
[16]HINTON G E.Learning Distributed Representations of Con-cepts[C]//Proceedings of theeighth Conference of the Cognitive Science Society.1989.
[17]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating Embeddings for Modeling Multi-Relational Data[C]//Proceedings of the Neural Information Processing Systems 26:27th Annual Conference on Neural Information Processing Systems 2013.2013:2787-2795.
[18]PEROZZI B,AL-RFOU R,SKIENA S.DeepWalk:OnlineLearning of Social Representations[C]//Proceedings of the 20th ACM SIGKDDInternational Conference on Knowledge Discoveryand Data Mining.2014:701-710.
[19]MIKOLOV T,SUTSKEVER I,CHEN K,et al[C]//Procee-dings of the 26th International Conference on Neural Information Processing Systems.2013:3111-3119.
[20]JADERBERG M,VEDALDI A,ZISSERMAN A.Deep Features for Text Spotting[C]//Proceedings of the European Conference on Computer Vision.2014:512-528.
[21]YANG X,MACDONALD C,OUNIS I.Using Word Embed-dings in Twitter Election Classification [J].Information Retrieval Journal,2018,21:183-207.
[22]LIMSOPATHAM N,COLLIER N H.Bidirectional Lstm forNamed Entity Recognition in Twitter Messages[C]//Procee-dings of the NUT@COLING 2016.2016:145-152.
[23]CHEN W,YEO C K,LAU C T,et al.Leveraging Social Media News to Predict Stock Index Movement Using RNN-Boos [J].Data Knowl.Eng,2018(118):14-24.
[24]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[J/OL].2018.https://tooob.com/api/objs/read/noteid/2871-7995/.
[25]SANH V,DEBUT L,CHAUMOND J,et al.Distilbert,a Distilled Version of BERT:Smaller,Faster,Cheaper and Lighter[OL].http://arxiv.org/abs/1910.01108.
[26]SUN C,QIU X P,XU Y G,et al.How To Fine-Tune BERT For Text Classification?[C]//Proceedings of the Chinese Computational Linguistics-18th China National Conference.Kunming,2019:194-206.
[27]CSURKA G,DANCE C R,FAN L X,et al.Visual Categorization With Bags Of Keypoints[C]//Proceedings of the Statistical Learning in Compu-ter Vision,European Conference on Computer Vision.2004:1-2.
[28]BOIMAN O,SHECHTMAN E,IRANI M.In Defense of Nearest-Neighbor Based Image Classification[C]//Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition.USA,2008.
[29]YIN H,JIAO X,CHAI Y,et al.Scene Classification Based on Single-Layer SAE And SVM[J].Expert Systems with Applications,2015,42(7):3368-3380.
[30]SONG J,ZHANG H,LI X,et al.Self-Supervised Video Hashing with Hierarchical Binary Auto-Encoder[J].IEEE Transactions on Image Processing,2018,PP(99):3210-3221.
[31]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImagenetClassification with Deep Convolutional Neural Networks[C]//Proceedings of the Neural Information Processing Systems 25:26th Annual Conference on Neural Information Processing Systems.Lake Tahoe,Nevada,United States,2012:1106-1114.
[32]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative Adversarial Nets.[C]//Proceedings of the Neural Information Processing Systems 27:Annual Conference on Neural Information.Montreal,Quebec,Canada,2014,2672-2680.
[33]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//Proceedings of the 3rd International Conference on Learning Representations.San Diego,CA,USA,2015.
[34]SZEGEDY C,LIU W,JIA Y,et al.Going Deeper with Convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA,2015,1-9.
[35]RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[C]//Proceedings of the4th International Conference on Learning Representations.Puerto Rico,2016.
[36]HARDOON D R,SZEDMAK S,SHAWE-TAYLOR J.Canonical Correlation Analysis:An Overview with Application to Learning Methods[J].Neural Computation,2004,16(12):2639-2664.
[37]RASIWASIA N,PEREIRA J C,COVIELLO E,et al.A New Approach to Cross-Modal Multimedia Retrieval[C]//Procee-dings of the 18th International Conference on Multimedea 2010.Firenze,Italy,2010.
[38]GUILLAUMIN M,VERBEEK J J,Schmid C.Multimodal semi-supervised learning for image classification[C]//Computer Vision & Pattern Recognition.2010.
[39]THEIL H,CHUNG C F.Relations between two sets of va-riates:The bits of information provided by each variate in each set[J].Statistics & Probability Letters,1988,6(3):137-139.
[40]WU F,ZHANG H,ZHUANG Y.Learning Semantic Correla-tions for Cross-Media Retrie-val[C]//Proceedings of the IEEE International Conference on Image Processing.IEEE,2007.
[41]PEREIRA J C,COVIELLO E,DOYLE G,et al.On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrie-val[J].IEEE Trans Pattern Anal Mach Intell,2014,36(3):521-535.
[42]AKAHO S.A Kernel Method for Canonical Correlation Analysis[OL].https://arxiv.org/abs/cs/0609071.
[43]ANDREW G,ARORA R,BILMES J,et al.Deep Canonical Correlation Analysis[C]//International Conference on International Conference on Machine Learning.2013.
[44]WANG W,ARORA R,LIVESCU K,et al.On Deep Multi-View Representation Learning:Objectives and Optimization[OL].https://arxiv.org/abs/1602.01024v1.
[45]HUANG P Y,LIANG J,LAMARE J B,et al.Multimodal Filtering of Social Media for Temporal Monitoring and Event Analysis[C]//Proceedings of the ACM.2018:450-457.
[46]BLEI D M,JORDAN M I.Modeling Annotated Data[C]//Proceedings of the 26th Annual International Conference on Research and Development in Information Retrieval.Toronto,Canada,2003:127-134.
[47]RAMAGE D,HEYMANN P,MANNING C D,et al.Clustering the Tagged Web[C]//Proceedings of the Second International Conference on Web Search and Web Data Mining.Barcelona,Spain,2009.
[48]SANG J T,XU C S.Right Buddy Makes the Difference:An Early Exploration of Social Relation Analysis in Multimedia Applications[C]//Proceedings of the 20th ACM Multimedia Confe-rence.Nara,Japan,2012:19-28.
[49]QIAN S,ZHANG T,XU C,et al.Multi-Modal Event TopicModel for Social Event Analysis[J].IEEE Transactions on Multimedia,2016,18(2):233-246.
[50]SANG J,XU C,JAIN R.Social Multimedia Ming:From Special to General[C]//Proceedings of the 2016 IEEE International Symposium on Multimedia.2016.
[51]LIU X L,HUET B.Heterogeneous Features and Model Selection for Event-Based Media Classification[C]//Proceedings of theInternational Conference on Multimedia Retrieval.Dallas,TX,USA,2013:151-158.
[52]DAS R,ZAHEER M,DYER C.Gaussian LDA for Topic Models with Word Embeddings[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Internatio-nal Joint Conference on Natural Language Processing.2015.
[53]WAN L,ZHU L,FERGUS R.A Hybrid NeuralNetwork-La-tent Topic Model[C]//Proceedings of the Fifteenth Internatio-nal Conference on ArtificialIntelligence and Statistics.La Palma,Canary Islands,Spain,2012:1287-1294.
[54]SRIVASTAVA N,SALAKHUTDINOV R.Multimodal Lear-ning with Deep Boltzmann Machines[C]//International Confe-rence on Neural Information Processing Systems.2012.
[55]NGIAM J,KHOSLA A,KIM M,et al.Multimodal Deep Lear-ning[C]//International Conference on Machine Learning.2009.
[56]GUO Q,JIA J,SHEN G,et al.Learning Robust Uniform Features for Cross-Media Social Data by Using Cross Autoencoders[J].Knowledge Based Systems,2016,102(15):64-75.
[57]FENG F,WANG X,LI R,et al.Correspondence Autoencoders for Cross-Modal Retrieval[J].Acm Transactions on Multimedia Computing Communications & Applications,2015,12(1s):26.
[58]FENG F,LI R,WANG X.Deep correspondence restricted Boltzmann machine for cross-modal retrieval[M].Amsterdam:Elsevier Science Publishers B.V.,2015.
[59]HONG S,IM W,YANG H S.Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint[C]//Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval.Yokohama,Japan,2018:353-361.
[60]WEI Y,ZHAO Y,LU C,et al.Cross-Modal Retrieval with CNN Visual Features:A New Baseline[J].IEEE Transactions on Cybernetics,2017,47(2):449-460.
[61]KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.Doha,Qatar,2014:1746-1751.
[62]HE Y,XIANG S,KANG C,et al.Cross-Modal Retrieval viaDeep and Bidirectional Representation Learning[J].IEEE Transactions on Multimedia,2016,18(7):1363-1377.
[63]HONG C Q,YU J.Multi-modal face pose estimation with multi-task manifold deep learning.[OL].http://arxiv.org/abs/1712.06467.
[64]GAO Y,ZHANG H,ZHAO X,et al.Event Classification in Microblogs via Social Tracking[J].Acm Transactions on Intelligent Systems & Technology,2017,8(3):35.
[65]ZHANG X,GHORBANI A A.An overview of online fakenews:Characterization,detection,and discussion[J].Information Processing & Management,2020,57(2):102025.
[66]VLACHOS A,RIEDEL S.Fact Checking:Task Definition and Dataset Construction[C]//Proceedings of the ACL 2014 Workshop on Language Technologies and Computational SocialScience.2014.
[67]MAGDY A,WANAS N,MAGDY A,et al.Web-based statistical fact checking of textual documents[C]//Proceedings of the 2nd international workshop on Search and mining user-generated contents.Toronto,ON,Canada,2010:103-110.
[68]ZHANG H W,FANG Q,QIAN S S,et al.Multi-Modal Know-ledge-Aware Event Memory Network for Social Media Rumor Detection[C]//Proceedings of the 27th ACM International Conference on Multimedia.Nice,France,2019:1942-1951.
[69]WANG Y Z,QIAN S S,HU J,et al.Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks[C]//Proceedings of the 2020 on International Conference on Multimedia Retrieval.Dublin,Ireland,2020:540-547.
[70]AFROZ S,BRENNAN M,GREENSTADT R.Detecting Hoa-xes,Frauds,And Deception in Writing Style Online[C]//Proceedings of the IEEE Symposium on Security and Privacy.San Francisco,California,2012:461-475.
[71]BENJAMIN D H,SIBEL A.This Just In:Fake News Packs a Lot in Title,Uses Simpler,Repetitive Content in Text Body,More Similar to Satire than Real News[OL].http://arxiv.org/abs/1703.09398v1.
[72]CASTILLO C,MENDOZA M,POBLETE B.Information credibility on Twitter[C]//Proceedings of the 20th International Conference on World Wide Web.Hyderabad,India,2011.
[73]JIN Z W, CAO J,ZHANG Y D,et al.News Verification by Exploiting Conflicting Social Viewpoints in Microblogs[C]//AAAI.2016:2972-2978.
[74]AHMED H.Detecting opinion spam and fake news usingn-gram analysis and semantic similarity[OL].https://onlinelibrary.wiley.com/doi/epdf/10.1002/spy2.9.
[75]ZHANG H W,QIAN S S,FANG Q,et al.Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection[J].IEEE Transaction on Multimedia,2020,PP(99):1-1.
[76]BAIRD S,SIBLEY D,PAN Y X.Talos targets disinformation with fake news challenge victory[OL].https://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html.
[77]HANSELOWSKI A,AVINESH P V S,SCHILLER B,et al.Description of the system developed by team athene in the fnc-1[OL].https://medium.com/@andre134679/team-athene-on-the-fake-news-challenge-28a5cf5e017b.
[78]RIEDEL B,AUGENSTEIN I,SPITHOURAKIS G P,et al.ASimple but Tough-To-Beat Baseline For The Fake News Challenge Stance Detection Task[OL].http://arxiv.org/pdf/1707.03264.
[79]POTTHAST M,KIESEL J,REINARTZ K,et al.A Stylometric Inquiry into Hyperpartisan and Fake News[OL].https://www.researchgate.net/profile/Martin_Potthast/publication/313861498_A_Stylometric_Inquiry_into_Hyperpartisan_and_Fake_News/links/58afe5aba6fdcc6f03f3675b/A-Stylometric-Inquiry-into-Hyperpartisan-and-Fake-News.pdf.
[80]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,2016:3818-3824.
[81]CHEN T,WU L,LI X,et al.Call attention to rumors:deep attention based recurrent neural networks for early rumor detection[C]//Proceedings of the Pacific-Asia Conference on Know-ledge Discovery and Data Mining.Australia,2018:40-52.
[82]YU F,LIU Q,WU S,et al.A Convolutional Approach for Misinformation Identification[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence.Melbourne,Australia,2017:3901-3907.
[83]JIN Z W,CAO J,GUO H,et al.Multimodal Fusion with Recurrent Neural Networks for Rumor Detection On Microblogs[C]//Proceedings of the 2017 ACM on Multimedia Conference.Mountain View,CA,USA,2017:795-816.
[84]YANG Y,ZHENG L,ZHANG J W,et al.TI-CNN:Convolutional Neural Networks for Fake News Detection[OL].https://arxiv.org/pdf/1806.00749.pdf.
[85]ZHOU X Y,WU J D,ZAFARANI R.SAFE:Similarity-Aware Multi-Modal Fake News Detection[OL].https://arxiv.org/abs/2003.04981.
[86]FERRARA E,VAROL O,DAVIS C,et al.The Rise of Social Bots[J].Communications of the Acm,2014,59(7):96-104.
[87]ZHAO J,CAO N,WEN Z,et al.FluxFlow:Visual Analysis of Anomalous Information Spreading on Social Media[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(12):1773-1782.
[88]MURTHY D,POWELL A,TINATI R,et al.Automation,algorithms,and politics| bots and political influence:a sociotechnical investigation of social network capital [J].International Journal of Communication,2016,10,4952-4971.
[89]MISLOVE A,MARCON M,GUMMADI P K,et al.Measure-ment and analysis of online social networks[C]//Proceedings of the Internet Measurement Conference 2007.2007:29-42.
[90]CHU Z,GIANVECCHIO S,WANG H,et al.Detecting Auto-mation of Twitter Accounts:Are You a Human,Bot,or Cyborg? [J].IEEE Transactions on Dependable & Secure Computing,2012,9(6):811-824.
[91]LIU Y,WU Y F B.Early Detection of Fake News on SocialMedia Through Propagation Path Classification with Recurrent and Convolutional Networks[C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.New Or-leans,Louisiana,USA,2018:354-361.
[92]MA J,GAO W,WONG K F.Rumor Detection on Twitter With Tree-Structured Recursive Neural Networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computatio-nal Linguistics.Melbourne,Australia,2018:1980-1989.
[93]BIAN T,XIAO X,XU T,et al.Rumor Detection on SocialMedia with Bi-Directional Graph Convolutional Networks [C]//Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(1):549-556.
[94]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.TX,USA,2013:1103-1108.
[95]JAMES A.Topic Detection and Tracking:Event Based Information Retrieval [J].Information Retrieval Journal,2002,5(2):139-157.
[96]MARON M E.Automatic Indexing:An Experimental Inquiry[J].Journal of the Acm,1961,8(3):404-417.
[97]POPESCU A M,PENNACCHIOTTI M.Detecting Controver-sial Events from Twitter[C]//Proceedings of the 19th ACM International Conference on Information and Knowledge Management.Toronto,Ontario,Canada,2010:1873-1876.
[98]LUITEL B,MURTHY Y V S,KOOLAGUDI S.Sound Event Detection in Urban Soundscape using Two-level Classification[C]//Proceedings of the 2016 IEEE Distributed Computing,VLSI,Electrical Circuits and Robotics.Mangalore,2016:259-263.
[99]SEEMA,WAZARKAR,BETTAHALLY,et al.Region-basedSegmentation of Social Images Using Soft KNN Algorithm [J].Procedia Computer Science,2018,125:93-98.
[100]SADLIER D A,O’CONNOR N E.Event Detection in FieldSports Video Using Audio-Visual Features and A Support Vector Machine [J].IEEE Transactions on Circuits and Systems for Video Technology,2005,5(10):1225-1233.
[101]REUTER T,CIMIANO P.Event-Based Classification of Social Media Streams[C]//Proceedings of the 2nd ACM International Conference on Multimedia Retrieval.Hong Kong,China,2012:22.
[102]BISCHKE B,BORTH D,SCHULZE C,et al.Contextual En-richment of Remote-Sensed Events with Social Media Streams[C]//Proceedings of the 24th ACM International Conference on Multimedia.Amsterdam,Netherlands,2016:1077-1081.
[103]BLANDFORT P,PATTON D U,FREY W R,et al.Multimodal social media analysis for gang violence prevention[C]//Procee-dings of the International AAAI Conference on Web and Social Media.Munich,Germany,2019,114-124.
[104]ZHAO S,YAO H,ZHAO S,et al.Multi-modal microblog classification via multi-task learning [J].Multimedia Tools & Applications,2016,75(15):8921-8938.
[105]SANKARANARAYANAN J,SAMET H,TEITLER B E,et al.Twitterstand:news in tweets[C]//Proceedings of the 17th Acm Sigspatial International Conference on Advances in Geographic Information Systems.Seattle,Washington,USA,2009:42-51.
[106]ZHU J,CHEN N,PERKINS H,et al.Gibbs Max-margin Topic Models with Data Augmentation [J].Journal of Machine Lear-ning Research,2013,15(1):1073-1110.
[107]FRED A L N,JOSÉ M N L.Partitional vs Hierarchical Clustering Using a Minimum Grammar Complexity Approach[C]//Joint Iapr International Workshops on Advances in Pattern Reco-gnition.Springer-Verlag,2000.
[108]JON R.Pattern Recognition:Concepts,Methods and Applica-tions [J/OL].Assembly Automation,https://doi.org/10.1108/aa.2002.03322dae.002.
[109]ZHAO Y C,SONG J D.Gdilc:A Grid-Based Density-IsolineClustering Algorithm[C]//Proceedings of the 2001 Internatio-nal Conferences on Info-Tech and Info-Net.Beijing,China,2001:140-145.
[110]BECKER H,NAAMAN M,GRAVANO L.Event Identification in Social Media[C]//Proceedings of the 12th International Workshop on the Web and Databases.Providence,Rhode Island,USA,2009:291-300.
[111]CHOI J,KIM E,LARSON M,et al.Evento 360:Social Event Discovery from Web-scale Multimedia Collection[C]//Procee-dings of the 23rd ACM International Conference on Multimedia.Brisbane,Australia,2015:193-196.
[112]MA Y,LI Q,YANG Z G,et al.An SVD-based multimodal clustering method for social event detection[C]//Proceedings of 31st International Conference on Data Engineering Workshops.Seoul,South Korea,2015:202-209.
[113]CAPDEVILA J,JESÚS CERQUIDES,NIN J,et al.Tweet-SCAN:An Event Discovery Technique for Geo-Located Tweets[C]//Proceedings of the 18th International Conference of the Catalan Association for Artificial Intelligence.Valencia,Catalonia,Spain,2015:110-119.
[114]CHU L Y,ZHANG Y Y,LI G R,et al.Effective Multimodality Fusion Framework for Cross-Media Topic Detection[J].IEEE Trans.Circuits Syst.Video Technol.J.,2016,26(3):556-569.
[115]ZHAO S,GAO Y,DING G,et al.Real-Time Multimedia Social Event Detection in Microblog [J].IEEE Transactions on Cybernetics,2017,1-14.
[116]KUMARAN G,ALLAN J.Text Classification and Named Entities For New Event Detection[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Sheffield,UK,2004:297-304.
[117]MERLER M,HUANG B,XIE L,et al.Semantic Model Vectors for Complex Video Event Recognition [J].IEEE Transactions on Multimedia,2012,14(1):88-101.
[118]ZHANG T,XU C.Cross-Domain Multi-Event Tracking via CO-PMHT [J].Acm Transactions on Multimedia Computing Communications & Applications,2014,10(4):31.
[119]WU X,NGO C W,HAUPTMANN A G.Multimodal News Story Clustering with Pairwise Visual Near-Duplicate Constraint [J].IEEE Transactions on Multimedia,2008,10(2):188-199.
[120]KALAMARAS I,DROSOU A,TZOVARAS D.Multi-objective optimization for multimodal visualization [J].IEEE Transactions on Multimedia.J.,2014,16(5):1460-1472.
[121]MAKKONEN J,AHONEN-MYKA H,SALMENKIVI M.Simple Semantics in Topic Detection and Tracking [J].Information Retrieval,2004,7(3/4):347-368.
[122]YANG Y M,ZHANG J,CARBONELL J G,et al.Topic-Conditioned Novelty Detection[C]//Proceedings of the Eighth ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.Edmonton,Alberta,Canada,2002:688-693.
[123]ALLAN J,WADE C,BOLIVAR A.Retrieval and Novelty Detection at the Sentence Level[C]//Proceedings of the 26th Annual International ACM SIGIRConference onResearch and Development in Information Retrieval.Canada,2003,314-321.
[124]WANG C,BLEI D,HECKERMAN D.Continuous Time Dynamic Topic Models[C]//Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence(UAI’08).2008:579-586.
[125]ROY S D,MEI T,ZENG W.Bridging Human-Centered Social Media Content Across Web Domains[M].Springer International Publishing,2014.
[126]KENDER J R,NAPHADE M R.Visual Concepts for News Story Tracking:Analyzing and Exploiting the Nist Trecvid Video Annotation Experiment[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego,CA,USA,2005:1174-1181.
[127]ZHAI Y,SHAH M.Tracking News Stories Across Different Sources[C]//Proceedings of the 13th ACM International Conference on Multimedia.Singapore,2005:2-10.
[128]QIAN S,ZHANG T,XU C.Online Multi-modal Multi-expert Learning for Social Event Tracking [J].IEEE Transactions on Multimedia,2018:1-1.
[129]ALSUMAIT L,BARBARA D,DOMENICONI C.Online LDA:adaptive topic model for mining text streams with application on topic detection and tracking[C]//Proceedings of the Eighth IEEE International Conference on Data Mining.New York,2008:3-12.
[130]YU B G,WANG L F,ZHANG W C.Topic Evolution Analysis Based on Dual-OLDA Model Under Chinese Semantic Environment[C]//Proceedings of the IEEE International Conference on Big Data Analysis.New York,2017:658-664.
[131]WANG Y,AGICHTEIN E,BENZI M.TM-LDA:Efficient Online Modeling of Latent Topic Transitions in Social Media[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Beijing,China,2012:123-131.
[132]SAHA A,SINDHWANI V.Learning Evolving and EmergingTopics in Social Media:A Dynamic NMF approach with Temporal Regularization[C]//Proceedings of the Fifth International Conference on Web Search and Web Data Mining.Seattle,WA,USA,2012:693-702.
[133]TANG L,LIU H,ZHANG J P,et al.Community Evolution in Dynamic Multi-Mode Networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.Las Vegas,Nevada,USA,2008:677-685.
[134]HUANG X H,YE Y M,XIONG L Y,et al.Clustering Time-Stamped Data Using Multiple Nonnegative Matrices Factorization [J].Knowledge Based Systems,2016,114:88-98.
[135]LIN Y R,SUN J,CASTRO P,et al.Metafac:community disco-very viarelational hypergraph factorization.Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Paris,France,2009:527-536.
[136]GUO X,XIANG Y,CHEN Q,et al.LDA-based online topic detection using tensor factorization [J].Journal of Information Science,2013,39(4):459-469.
[137]ZHANG S.Research on Text Classification of Internet Public Opinion Based on SVM [D].Qufu:Qufu Normal University,2015.
[138]MA M,LIU D S,LI H.Research on the Network Public Opinion Analysis System Model Based on Big Data [J].Information Science,2016,36(3):25-28.
[139]HAN Y X.Research on Weibo Opinion Detection Method Based on Neural Network [D].Xinjiang:Xinjiang University,2019.
[140]TIAN J J,LAN Y X,XIA Y X.Recognition and Empirical Research of Network Public Opinion Reversal Based on Decision Tree Method [J].Journal of Intelligence,2019,38(8):121-125.
[141]ZHANG Q H.Design and Implementation of Short MessageMonitoring System Based on Lingo Algorithm[D].Beijing:Beijing University of Posts and Telecommunications,2012.
[142]NIE F Y.Research on the Classification Model of Public Opinion Based on Fuzzy C Means[J].Software Guide,2017,16(6):130-132.
[143]ZHANG H P,CHEN Q H.Research on the Prediction of Network Public Opinion Based on Grey Markov Model.[J].Information Science,2018,36(1):75-79.
[144]HE Y X,LIU J B,SUN S T.Neural Network-Based PublicOpinion Prediction Method for Microblog[J].Journal of South China University of Technology(Natural Science Edition,2016(44):52.
[145]KUMAR S,BARBIER G,ABBASI M A,et al.TweetTracker:An Analysis Tool for Humanitarian and Disaster Relief[C]//International Conference on Weblogs & Social Media.2011.
[146]SHEKHAR H,SETTY S.Disaster Analysis Through Tweets[C]//Proceedings of 2015 International Conference on Advances in Computing,Communications and Informatics.Kochi,India,2015:1719-1723.
[147]STOWE K,PAUL M J,PALMER M,et al.Identifying and Cate-gorizing Disaster-Related Tweets[C]//Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media.Austin,TX,USA,2016:1-6.
[148]TO H,AGRAWAL S,KIM S H,et al.On Identifying Disaster-Related Tweets:Matching-based or Learning-based?[C]//Proceedings of 2017 IEEE Third International Conference on Multimedia Big Data.Laguna Hills,CA,USA,2017:330-337.
[149]AHMAD K,RIEGLER M,POGORELOV K,et al.JORD:ASystem for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery[C]//Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing.Florence,Italy,2017:1-12.
[150]LI X K,CARAGEA D,ZHANG H Y,et al.Localizing andQuantifying Damage in Social Media Images[C]//Proceedings of 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.Barcelona,Spain,2018:194-201.
[151]NALLURU G,PANDEY R,PUROHIT H.Relevancy classification of multimodal social media streams for emergency ser-vices[C]//Proceedings of 2019 IEEE International Conference on Smart Computing.Washington,DC,USA,2019:121-125.
[152]ABAVISANI M,WU L,HU S,et al.Multimodal Categorization of Crisis Events in Social Media[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle,WA,USA,2020:14667-14677.
[153]JREIS J C S,MELO P D F,GARIMELLA K,et al.A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections[C]//Proceedings of the Fourteenth International AAAI Conference on Web and Social Media.USA,2020:903-908.
[154]SALEM F K A,FEEL R A,ELBASSUONI S,et al.FA-KES:A Fake News Dataset around the Syrian War[C]//Proceedings of the Thirteenth International Conference on Web and Social Media.Munich,Germany,2019:573-582.
[155]HORNE B D,ADALI S.This Just in:Fake News Packs a Lot in Title,Uses Simpler,Repetitive Content in Text Body,More Similar to Satire than Real News[OL].https://arxiv.org/abs/1703.09398v1.
[156]BURFOOT C,BALDWIN T.Automatic Satire Detection:Are You Having a Laugh?[C]//Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP.Singapore,2009:161-164.
[157]NØRREGAARD J,HORNE B D,ADALI S.NELA-GT-2018:A Large Multi-Labelled News Dataset for the Study of Misinformation in News Articles[OL].http://arxiv.org/abs/1904.01546v1.
[158]SHU K,SLIVA A,WANG S,et al.Fake News Detection on Social Media:A Data Mining Perspective[J/OL].ACM SIGKDD Explorations Newsletter,https://www.researchgate.net/profile/Kai-Shu/publication/318981549_Fake_News_Detection_on_Social_Media_A_Data_Mining_Perspective/links/59da74eaa-ca272e6096bead4/Fake-News-Detection-on-Social-Media-A-Data-Mining-Perspective.pdf.
[159]ZUBIAGA A,LIAKATA M,PROCTER R,et al.AnalysingHow People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads[J].PLoS ONE,2016,11(3).
[160]WANG W Y.“Liar,Liar Pants on Fire”:A New Benchmark Dataset for Fake News Detection[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Vancouver,Canada,2017:422-426.
[161]MITRA T.CREDBANK:A Large-Scale Social Media Corpus With Associated Credibility Annotations[C]//Proceedings of the Ninth International Conference on Web and Social Media.Oxford,UK,2015:258-267.
[162]PAPADOPOULOS S,SCHINAS E,MEZARIS V,et al.Social Event Detection at MediaEval 2011:Challenges,Dataset and Evaluation[J].Mediaeval Workshop,2016:18-19.
[163]PAPADOPOULOS S,SCHINAS E,MEZARIS V,et al.The2012 Social Event Detection Dataset[C]//Proceedings of the 4th ACM Multimedia Systems Conference.Oslo,Norway,2013:102-107.
[164]REUTER T,PAPADOPOULOS S,PETKOS G,et al.SocialEvent Detection at Mediaeval 2013:Challenges,Datasets and Evaluation[J].In Medieval 2013 Workshop,2013:18-19.
[165]PETKOS G,PAPADOPOULOS S,MEZARIS V,et al.SocialEvent Detection at Mediaeval 2014:Challenges,Datasets and Evaluation[C]//Proceedings of the Medieval 2014 Workshop.Barcelona,Catalunya,Spain,2014.
[166]GAO Y,WANG F,LUAN H,et al.Brand Data Gathering from Live Social Media Streams[C]//Proceedings of the International Conference on Multimedia Retrieval.Glasgow,United Kingdom,2014,169.
[167]QIAN S S,ZHANG T Z,XU C S.Multi-modal Multi-view To-pic-opinion Mining for Social Event Analysis[C]//Proceedings of the 2016 ACM Conference on Multimedia Conference.Amsterdam,Netherlands,2016:2-11.
[168]XUE F,HONG R,HE X,et al.Knowledge-Based Topic Model for Multi-Modal Social Event Analysis [J].IEEE Transactions on Multimedia,2020,22(8):2098-2110.
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