Survey on anomaly propagation research for social networks

LU Kun; ZHANG Jiayu; ZHANG Hongli; FANG Binxing

doi:10.11959/j.issn.1000-436x.2024045

您当前的位置：

首页 >

文章列表页 >

Survey on anomaly propagation research for social networks

Comprehensive Review | 更新时间：2024-06-24

- Survey on anomaly propagation research for social networks
- Journal on Communications Vol. 45, Issue 5, Pages: 191-213(2024)
- 作者机构：
  
  1.哈尔滨工业大学网络空间安全学院，黑龙江哈尔滨 150001
  2.广州大学网络空间先进技术研究院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2024045
  CLC： TP391
- Received：10 November 2023，
  
  Revised：2024-01-17，
  
  Published：30 May 2024
- 稿件说明：
移动端阅览
卢昆,张嘉宇,张宏莉等.面向社交网络的异常传播研究综述[J].通信学报,2024,45(05):191-213.

LU Kun,ZHANG Jiayu,ZHANG Hongli,et al.Survey on anomaly propagation research for social networks[J].Journal on Communications,2024,45(05):191-213.
卢昆,张嘉宇,张宏莉等.面向社交网络的异常传播研究综述[J].通信学报,2024,45(05):191-213. DOI： 10.11959/j.issn.1000-436x.2024045.

LU Kun,ZHANG Jiayu,ZHANG Hongli,et al.Survey on anomaly propagation research for social networks[J].Journal on Communications,2024,45(05):191-213. DOI： 10.11959/j.issn.1000-436x.2024045.

摘要

异常传播是当今在线社交网络中频繁出现的一种非传统的信息传播模式。为了完整地认知社交网络中异常传播的整体过程，将异常传播生命周期系统归纳为潜伏期、扩散期、高潮期和衰退期4个阶段。针对异常传播在不同阶段所存在的科学问题，从微观和宏观视角分别定义和划分了异常信息、异常用户以及异常传播、传播抑制4个当前热门的研究领域，详细综述了当前4个研究领域下的主要研究任务以及相关研究进展，并分析了现有方法存在的问题，对社交网络异常传播领域的未来研究方向进行了展望，为后续研究提供便利。

Abstract

Anomaly propagation is a non-traditional mode of information dissemination that frequently occurs in today’s online social networks. To fully comprehend the overall process of anomaly propagation in social networks

the anomaly propagation lifecycle system was classified into incubation period

diffusion period

climax period

and decline period. In response to the scientific problems existing in different stages of anomaly propagation

four popular research fields of anomaly information

anomaly users

anomaly propagation

and propagation containment were defined and divided respectively from micro and macro perspectives. The main research tasks and related research progress in the four current research areas were reviewed and summarized in detail

problems in existing methods were analyzed. The future research directions on anomaly propagation in social networks were prospected

providing convenience for subsequent research.

关键词

Keywords

references

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 Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2018 : 1980 - 1989 .

LAZER D M J , BAUM M A , BENKLER Y , et al . The science of fake news [J ] . Science , 2018 , 359 ( 6380 ): 1094 - 1096 .

HU X , TANG J L , ZHANG Y C , et al . Social spammer detection in microblogging [C ] // Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence . New York : ACM Press , 2013 : 2633 - 2639 .

CHANDOLA V , BANERJEE A , KUMAR V . Anomaly detection: a survey [J ] . ACM Computing Surveys , 2009 , 41 ( 3 ): 15 .

LI J , SUN A X , HAN J L , et al . A survey on deep learning for named entity recognition [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 1 ): 50 - 70 .

GUO B , DING Y S , YAO L N , et al . The future of false information detection on social media: new perspectives and trends [J ] . ACM Computing Surveys , 2021 , 53 ( 4 ): 68 .

SHUMOVSKAIA V , KAYAALP M , SAYED A H . Identifying opinion influencers over social networks [C ] // Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2023 : 1 - 5 .

FENG S B , WAN H R , WANG N N , et al . TwiBot-20: a comprehensive twitter bot detection benchmark [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2021 : 4485 - 4494 .

BAKSHY E , ROSENN I , MARLOW C , et al . The role of social networks in information diffusion [C ] // Proceedings of the 21st International Conference on World Wide Web . New York : ACM Press , 2012 : 519 - 528 .

WU J M , ZHAO L M , LI D W , et al . Deeply exploit visual and language information for social media popularity prediction [C ] // Proceedings of the 30th ACM International Conference on Multimedia . New York : ACM Press , 2022 : 7045 - 7049 .

DALEY D J , KENDALL D G . Epidemics and rumours [J ] . Nature , 1964 , 204 ( 4963 ): 1118 .

BUDAK C , AGRAWAL D , EL ABBADI A . Limiting the spread of misinformation in social networks [C ] // Proceedings of the 20th International Conference on World Wide Web . New York : ACM Press , 2011 : 665 - 674 .

ZAREIE A , SAKELLARIOU R . Minimizing the spread of misinformation in online social networks: a survey [J ] . Journal of Network and Computer Applications , 2021 , 186 : 103094 .

VOSOUGHI S , ROY D , ARAL S . The spread of true and false news online [J ] . Science , 2018 , 359 ( 6380 ): 1146 - 1151 .

PENG N , DREDZE M . Supplementary results for named entityrecog nition on Chinese social media with an updated dataset [R ] . 2017 .

FININ T , MURNANE W , KARANDIKAR A , et al . Annotating named entities in Twitter data with crowdsourcing [C ] // Proceedings of the 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk . New York : ACM Press , 2010 : 80 - 88 .

CHITICARIU L , KRISHNAMURTHY R , LI Y Y , et al . Domain adaptation of rule-based annotators for named-entity recognition tasks [C ] // Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing . New York : ACM Press , 2010 : 1002 - 1012 .

LI C L , WENG J S , HE Q , et al . TwiNER: named entity recognition in targeted twitter stream [C ] // Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2012 : 721 - 730 .

CLAESER D , KENT S , FELSKE D . Multilingual named entity recognition on spanish-english code-switched tweets using support vector machines [C ] // Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching . Stroudsburg : Association for Computational Linguistics , 2018 : 132 - 137 .

SIKDAR U K , GAMBÄCK B . Feature-rich twitter named entity recognition and classification [C ] // Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT) . Osaka: The COLING 2016 Organizing Committee , 2016 : 164 - 170 .

LIMSOPATHAM N , COLLIER N . Bidirectional LSTM for named entity recognition in twitter messages [C ] // Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT) . Osaka: The COLING 2016 Organizing Committee , 2016 : 145 - 152 .

HUANG Z H , XU W , YU K . Bidirectional LSTM-CRF models for sequence tagging [J ] . arXiv Preprint , arXiv: 1508.01991 , 2015 .

NIE Y Y , TIAN Y H , WAN X , et al . Named entity recognition for social media texts with semantic augmentation [C ] // Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Stroudsburg : Association for Computational Linguistics , 2020 : 1383 - 1391 .

XU J J , HE H F , SUN X , et al . Cross-domain and semisupervised named entity recognition in Chinese social media: a unified model [J ] . IEEE/ACM Transactions on Audio , Speech and Language Processing, 2018 , 26 ( 11 ): 2142 - 2152 .

李明扬 , 孔芳 . 融入自注意力机制的社交媒体命名实体识别 [J ] . 清华大学学报(自然科学版) , 2019 , 59 ( 6 ): 461 - 467 .

LI M Y , KONG F . Combined self-attention mechanism for named entity recognition in social media [J ] . Journal of Tsinghua University (Science and Technology) , 2019 , 59 ( 6 ): 461 - 467 .

ZHANG Q , FU J L , LIU X Y , et al . Adaptive co-attention network for named entity recognition in tweets [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2018 , 32 ( 1 ): 5674 - 5681 .

SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition [J ] . arXiv Preprint , arXiv: 1409.1556 , 2014 .

YU J F , JIANG J , YANG L , et al . Improving multimodal named entity recognition via entity span detection with unified multimodal transformer [C ] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2020 : 3342 - 3352 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [J ] . arXiv Preprint , arXiv: 1706.03762 , 2017 .

WU Z W , ZHENG C M , CAI Y , et al . Multimodal representation with embedded visual guiding objects for named entity recognition in social media posts [C ] // Proceedings of the 28th ACM International Conference on Multimedia . New York : ACM Press , 2020 : 1038 - 1046 .

HE K M , GKIOXARI G , DOLLÁR P , et al . Mask R-CNN [C ] // Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2017 : 2980 - 2988 .

LU J Y , ZHANG D X , ZHANG P J . Flat multi-modal interaction transformer for named entity recognition [C ] // Proceedings of the 29th International Conference on Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2022 : 2055 - 2064 .

LI X N , YAN H , QIU X P , et al . FLAT: Chinese NER using flat-lattice transformer [C ] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2020 : 6836 - 6842 .

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 : ACM Press , 2016 : 3818 - 3824 .

KOCHKINA E , LIAKATA M , ZUBIAGA A . All-in-one: multi-task learning for rumour verification [J ] . arXiv Preprint , arXiv: 1806.03713 , 2018 .

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 . Stroudsburg : Association for Computational Linguistics , 2017 : 708 - 717 .

POTTHAST M , KIESEL J , REINARTZ K , et al . A stylometric inquiry into hyperpartisan and fake news [J ] . arXiv Preprint , arXiv: 1702.05638 , 2017 .

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 . Stroudsburg : Association for Computational Linguistics , 2017 : 422 - 426 .

MITRA T , GILBERT E . CREDBANK: a large-scale social media corpus with associated credibility annotations [J ] . Proceedings of the International AAAI Conference on Web and Social Media , 2021 , 9 ( 1 ): 258 - 267 .

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 .

DIFONZO N , BORDIA P. Rumor , gossip and urban legends [J ] . Diogenes , 2007 , 54 ( 1 ): 19 - 35 .

ZUBIAGA A , AKER A , BONTCHEVA K , et al . Detection and resolution of rumours in social media: a survey [J ] . ACM Computing Surveys , 51 ( 2 ): 1 - 36 .

CASTILLO C , MENDOZA M , POBLETE B . Information credibility on twitter [C ] // Proceedings of the 20th International Conference on World Wide Web . New York : ACM Press , 2011 : 675 - 684 .

KWON S , CHA M , JUNG K , et al . Prominent features of rumor propagation in online social media [C ] // Proceedings of the 2013 IEEE 13th International Conference on Data Mining . Piscataway : IEEE Press , 2013 : 1103 - 1108 .

SUN M Z , ZHANG X , ZHENG J Q , et al . DDGCN: dual dynamic graph convolutional networks for rumor detection on social media [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2022 , 36 ( 4 ): 4611 - 4619 .

LI C , PENG H , LI J X , et al . Joint stance and rumor detection in hierarchical heterogeneous graph [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 33 ( 6 ): 2530 - 2542 .

ZHENG J Q , ZHANG X , GUO S C , et al . MFAN: multi-modal feature-enhanced attention networks for rumor detection [C ] // Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence . California : International Joint Conferences on Artificial Intelligence Organization , 2022 : 2413 - 2419 .

张志勇 , 荆军昌 , 李斐 , 等 . 人工智能视角下的在线社交网络虚假信息检测、传播与控制研究综述 [J ] . 计算机学报 , 2021 , 44 ( 11 ): 2261 - 2282 .

ZHANG Z Y , JING J C , LI F , et al . Survey on fake information detection, propagation and control in online social networks from the perspective of artificial intelligence [J ] . Chinese Journal of Computers , 2021 , 44 ( 11 ): 2261 - 2282 .

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 . New York : ACM Press , 2012 : 1 - 7 .

LIU X M , NOURBAKHSH A , LI Q Z , et al . Real-time rumor debunking on twitter [C ] // Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . New York : ACM Press , 2015 : 1867 - 1870 .

ZHAO Z , RESNICK P , MEI Q Z . Enquiring minds: early detection of rumors in social media from enquiry posts [C ] // Proceedings of the 24th International Conference on World Wide Web . New York : ACM Press , 2015 : 1395 - 1405 .

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 . California : International Joint Conferences on Artificial Intelligence Organization , 2017 : 3901 - 3907 .

KHOO L M S , CHIEU H L , QIAN Z , et al . Interpretable rumor detection in microblogs by attending to user interactions [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 5 ): 8783 - 8790 .

BIAN T , XIAO X , XU T Y , et al . Rumor detection on social media with Bi-directional graph convolutional networks [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 1 ): 549 - 556 .

LIN H Z , MA J , CHENG M F , et al . Rumor detection on twitter with claim-guided hierarchical graph attention networks [C ] // Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Stroudsburg : Association for Computational Linguistics , 2021 : 10035 - 10047 .

YUAN C Y , MA Q W , ZHOU W , et al . Jointly embedding the local and global relations of heterogeneous graph for rumor detection [C ] // Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE Press , 2019 : 796 - 805 .

ZHOU X , ZAFARANI R . A survey of fake news: fundamental theories, detection methods, and opportunities [J ] . ACM Computing Surveys (CSUR) , 2020 , 53 ( 5 ): 1 - 40 .

PARIKH S B , ATREY P K . Media-rich fake news detection: a survey [C ] // Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) . Piscataway : IEEE Press , 2018 : 436 - 441 .

GUO C , CAO J , ZHANG X , et al . Exploiting emotions for fake news detection on social media [J ] . arXiv Preprint , arXiv: 1903.01728 , 2019 .

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 . New York : ACM Press , 2019 : 877 - 880 .

ZHANG X Y , CAO J , LI X R , et al . Mining dual emotion for fake news detection [C ] // Proceedings of the Web Conference 2021 . New York : ACM Press , 2021 : 3465 - 3476 .

DOU Y T , SHU K , XIA C Y , et al . User preference-aware fake news detection [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2021 : 2051 - 2055 .

SUN L , RAO Y , LAN Y Q , et al . HG-SL: jointly learning of global and local user spreading behavior for fake news early detection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2023 , 37 ( 4 ): 5248 - 5256 .

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 , 2017 , 19 ( 3 ): 598 - 608 .

QI P , CAO J , YANG T Y , et al . Exploiting multi-domain visual information for fake news detection [C ] // Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE Press , 2019 : 518 - 527 .

CHEN X R , DONG C B , JI J Q , et al . Image manipulation detection by multi-view multi-scale supervision [C ] // Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2021 : 14165 - 14173 .

WANG Y Q , MA F L , JIN Z W , 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 . New York : ACM Press , 2018 : 849 - 857 .

KHATTAR D , GOUD J S , GUPTA M , et al . MVAE: multimodal variational autoencoder for fake news detection [C ] // Proceedings of the World Wide Web Conference . New York : ACM Press , 2019 : 2915 - 2921 .

ZHOU X Y , WU J D , ZAFARANI R . Safe: similarity-aware multi-modal fake news detection [J ] . arXiv Preprint , arXiv: 200304981 , 2020 .

CHEN Y X , LI D S , ZHANG P , et al . Cross-modal ambiguity learning for multimodal fake news detection [C ] // Proceedings of the ACM Web Conference 2022 . New York : ACM Press , 2022 : 2897 - 2905 .

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 International Conference on Multimedia Retrieval . New York : ACM Press , 2020 : 540 - 547 .

DUN Y Q , TU K F , CHEN C , et al . KAN: knowledge-aware attention network for fake news detection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2021 , 35 ( 1 ): 81 - 89 .

FREEMAN L C . Centrality in social networks conceptual clarification [J ] . Social Networks , 1978 , 1 ( 3 ): 215 - 239 .

BRIN S , PAGE L . The anatomy of a large-scale hypertextual Web search engine [J ] . Computer Networks and ISDN Systems , 1998 , 30 ( 1-7 ): 107 - 117 .

JACCARD P . Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines [J ] . Bulletin De La Societe Vaudoise Des Sciences Naturelles , 1901 , 37 ( 140 ): 241 - 272 .

闫光辉 , 张萌 , 罗浩 , 等 . 融合高阶信息的社交网络重要节点识别算法 [J ] . 通信学报 , 2019 , 40 ( 10 ): 109 - 118 .

YAN G H , ZHANG M , LUO H , et al . Identifying vital nodes algorithm in social networks fusing higher-order information [J ] . Journal on Communications , 2019 , 40 ( 10 ): 109 - 118 .

HU Y , WANG S S , REN Y Z , et al . User influence analysis for Github developer social networks [J ] . Expert Systems with Applications , 2018 , 108 : 108 - 118 .

ZHAO G S , LEI X J , QIAN X M , et al . Exploring users’ internal influence from reviews for social recommendation [J ] . IEEE Transactions on Multimedia , 2019 , 21 ( 3 ): 771 - 781 .

REZAIE B , ZAHEDI M , MASHAYEKHI H . Measuring time-sensitive user influence in Twitter [J ] . Knowledge and Information Systems , 2020 , 62 ( 9 ): 3481 - 3508 .

BERNA A G . Ranking influencers of social networks by semantic kernels and sentiment information [J ] . Expert Systems with Applications , 2021 , 171 : 114599 .

QIU J Z , TANG J , MA H , et al . DeepInf: social influence prediction with deep learning [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . New York : ACM Press , 2018 : 2110 - 2119 .

ZHENG C , ZHANG Q , YOUNG S , et al . On-demand influencer discovery on social media [C ] // Proceedings of the 29th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2020 : 2337 - 2340 .

AROUS I , YANG J , KHAYATI M , et al . OpenCrowd: a human-AI collaborative approach for finding social influencers via open-ended answers aggregation [C ] // Proceedings of the Web Conference 2020 . New York : ACM Press , 2020 : 1851 - 1862 .

ZHUANG H W , ZHOU B , XI W , et al . Modeling connection strength in graph neural networks for social influence prediction [C ] // Proceedings of the 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC) . Piscataway : IEEE Press , 2021 : 8 - 15 .

SONG L , WANG H B , ZHANG G Y , et al . FedInf: social influence prediction with federated learning [J ] . Neurocomputing , 2023 , 548 ( 1 ): 1 - 11 .

LEE K , CAVERLEE J , WEBB S . Uncovering social spammers: social honeypots + machine learning [C ] // Proceedings of the 33rd international ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2010 : 435 - 442 .

ZHU Y , WANG X , ZHONG E H , et al . Discovering spammers in social networks [J ] . Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence , 2012 , 26 ( 1 ): 171 - 177 .

YU D G , CHEN N , JIANG F , et al . Constrained NMF-based semi-supervised learning for social media spammer detection [J ] . Knowledge-Based Systems , 2017 , 125 : 64 - 73 .

HU X , TANG J L , GAO H J , et al . Social spammer detection with sentiment information [C ] // Proceedings of the 2014 IEEE International Conference on Data Mining . Piscataway : IEEE Press , 2014 : 180 - 189 .

RATHORE S , LOIA V , PARK J H . SpamSpotter: an efficient spammer detection framework based on intelligent decision support system on Facebook [J ] . Applied Soft Computing , 2018 , 67 : 920 - 932 .

YANG C , HARKREADER R , ZHANG J L , et al . Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter [C ] // Proceedings of the 21st International Conference on World Wide Web . New York : ACM Press , 2012 : 71 - 80 .

FAZIL M , ABULAISH M . A hybrid approach for detecting automated spammers in twitter [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 11 ): 2707 - 2719 .

张艳梅 , 黄莹莹 , 甘世杰 , 等 . 基于贝叶斯模型的微博网络水军识别算法研究 [J ] . 通信学报 , 2017 , 38 ( 1 ): 44 - 53 .

ZHANG Y M , HUANG Y Y , GAN S J , et al . Weibo spammers’ identification algorithm based on Bayesian model [J ] . Journal on Communications , 2017 , 38 ( 1 ): 44 - 53 .

CHEN C , ZHANG J , XIE Y , et al . A performance evaluation of machine learning-based streaming Spam tweets detection [J ] . IEEE Transactions on Computational Social Systems , 2015 , 2 ( 3 ): 65 - 76 .

FAKHRAEI S , FOULDS J , SHASHANKA M , et al . Collective spammer detection in evolving multi-relational social networks [C ] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM Press , 2015 : 1769 - 1778 .

JIANG W J , WU J , LI F , et al . Trust evaluation in online social networks using generalized network flow [J ] . IEEE Transactions on Computers , 2016 , 65 ( 3 ): 952 - 963 .

WU Y J , LIAN D F , XU Y H , et al . Graph convolutional networks with Markov random field reasoning for social spammer detection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 1 ): 1054 - 1061 .

GUO Z W , SHEN Y , BASHIR A K , et al . Robust spammer detection using collaborative neural network in Internet-of-things applications [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 12 ): 9549 - 9558 .

QIU T , LIU X Z , ZHOU X B , et al . An adaptive social spammer detection model with semi-supervised broad learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 10 ): 4622 - 4635 .

KOGGALAHEWA D , XU Y , FOO E . An unsupervised method for social network spammer detection based on user information interests [J ] . Journal of Big Data , 2022 , 9 ( 1 ): 1 - 35 .

GUO Z W , YU K P , JOLFAEI A , et al . Fuz-spam: label smoothing-based fuzzy detection of spammers in Internet of things [J ] . IEEE Transactions on Fuzzy Systems , 2022 , 30 ( 11 ): 4543 - 4554 .

DAVIS C A , VAROL O , FERRARA E , et al . BotOrNot: a system to evaluate social bots [C ] // Proceedings of the 25th International Conference Companion on World Wide Web . New York : ACM Press , 2016 : 273 - 274 .

FERRARA E , VAROL O , DAVIS C , et al . The rise of social bots [J ] . Communications of the ACM , 2016 , 59 ( 7 ): 96 - 104 .

CRESCI S , DI PIETRO R , PETROCCHI M , et al . Fame for sale: efficient detection of fake Twitter followers [J ] . Decision Support Systems , 2015 , 80 : 56 - 71 .

CRESCI S , DI PIETRO R , PETROCCHI M , et al . The paradigm-shift of social spambots: evidence, theories, and tools for the arms race [C ] // Proceedings of the 26th International Conference on World Wide Web Companion . New York : ACM Press , 2017 : 963 - 972 .

GILANI Z , FARAHBAKHSH R , TYSON G , et al . Of bots and humans (on Twitter) [C ] // Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . New York : ACM Press , 2017 : 349 - 354 .

YANG K C , VAROL O , HUI P M , et al . Scalable and generalizable social bot detection through data selection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 1 ): 1096 - 1103 .

FENG S B , TAN Z X , WAN H R , et al . TwiBot-22: towards graph-based twitter bot detection [J ] . Advances in Neural Information Processing Systems , 2022 , 35 : 35254 - 35269 .

VAROL O , FERRARA E , DAVIS C , et al . Online human-bot interactions: detection, estimation, and characterization [J ] . Proceedings of the International AAAI Conference on Web and Social Media , 2017 , 11 ( 1 ): 280 - 289 .

HEIDARI M , JONES J H , UZUNER O . Deep contextualized word embedding for text-based online user profiling to detect social bots on twitter [C ] // Proceedings of the 2020 International Conference on Data Mining Workshops (ICDMW) . Piscataway : IEEE Press , 2020 : 480 - 487 .

RODRIGUES A P , FERNANDES R , A A , et al . Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques [J ] . Computational Intelligence and Neuroscience , 2022 , 2022 : 5211949 .

KUDUGUNTA S , FERRARA E . Deep neural networks for bot detection [J ] . Information Sciences , 2018 , 467 : 312 - 322 .

FAZIL M , SAH A K , ABULAISH M . DeepSBD: a deep neural network model with attention mechanism for SocialBot detection [J ] . IEEE Transactions on Information Forensics and Security , 2021 , 16 : 4211 - 4223 .

CRESCI S . A decade of social bot detection [J ] . Communications of the ACM , 2020 , 63 ( 10 ): 72 - 83 .

LE T , TRAN-THANH L , LEE D . Socialbots on fire: modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning [C ] // Proceedings of the ACM Web Conference 2022 . New York : ACM Press , 2022 : 545 - 554 .

MAGELINSKI T , BESKOW D , CARLEY K M . Graph-hist: graph classification from latent feature histograms with application to bot detection [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 4 ): 5134 - 5141 .

ALI ALHOSSEINI S , BIN TAREAF R , NAJAFI P , et al . Detect me if you can: Spam bot detection using inductive representation learning [C ] // Proceedings of the 2019 World Wide Web Conference . New York : ACM Press , 2019 : 148 - 153 .

FENG S B , WAN H R , WANG N N , et al . SATAR: a self-supervised approach to twitter account representation learning and its application in bot detection [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2021 : 3808 - 3817 .

FENG S B , WAN H R , WANG N N , et al . BotRGCN: twitter bot detection with relational graph convolutional networks [C ] // Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . New York : ACM Press , 2021 : 236 - 239 .

FENG S B , TAN Z X , LI R , et al . Heterogeneity-aware twitter bot detection with relational graph transformers [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2022 , 36 ( 4 ): 3977 - 3985 .

LEI Z Y , WAN H R , ZHANG W Q , et al . BIC: twitter bot detection with text-graph interaction and semantic consistency [J ] . arXiv Preprint , arXiv: 2208.08320 , 2022 .

YANG Y G , YANG R Y , PENG H , et al . FedACK: federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detection [C ] // Proceedings of the ACM Web Conference 2023 . New York : ACM Press , 2023 : 1314 - 1323 .

MARTIN T , HOFMAN J M , SHARMA A , et al . Exploring limits to prediction in complex social systems [C ] // Proceedings of the 25th International Conference on World Wide Web . New York : ACM Press , 2016 : 683 - 694 .

曹婍 , 沈华伟 , 高金华 , 等 . 基于深度学习的流行度预测研究综述 [J ] . 中文信息学报 , 2021 , 35 ( 2 ): 1 - 18, 32 .

CAO Q , SHEN H W , GAO J H , et al . Survey on deep learning based popularity prediction [J ] . Journal of Chinese Information Processing , 2021 , 35 ( 2 ): 1 - 18, 32 .

BAO P , SHEN H W , HUANG J M , et al . Popularity prediction in microblogging network: a case study on sina weibo [C ] // Proceedings of the 22nd International Conference on World Wide Web . New York : ACM Press , 2013 : 177 - 178 .

SHEN H W , WANG D S , SONG C M , et al . Modeling and predicting popularity dynamics via reinforced Poisson processes [C ] // Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press , 2014 : 291 - 297 .

CAO Q , SHEN H W , CEN K T , et al . DeepHawkes: bridging the gap between prediction and understanding of information cascades [C ] // Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . New York : ACM Press , 2017 : 1149 - 1158 .

WU B , CHENG W H , ZHANG Y D , et al . Sequential prediction of social media popularity with deep temporal context networks [C ] // Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence . California : International Joint Conferences on Artificial Intelligence Organization , 2017 : 3062 - 3068 .

WU B , CHENG W H , LIU P Y , et al . SMP challenge: an overview of social media prediction challenge 2019 [C ] // Proceedings of the 27th ACM International Conference on Multimedia . New York : ACM Press , 2019 : 2667 - 2671 .

WANG C , GONG W Z , GAO X F , et al . An attention-based neural model for popularity prediction in social service [C ] // Proceedings of the 2020 IEEE International Conference on Web Services (ICWS) . Piscataway : IEEE Press , 2020 : 516 - 523 .

XIN S , LI Z , ZOU P C , et al . ATNN: adversarial two-tower neural network for new item’s popularity prediction in E-commerce [C ] // Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2021 : 2499 - 2510 .

OU N R , YU L , LI H Y , et al . MTAF: shopping guide micro-videos popularity prediction using multimodal and temporal attention fusion approach [C ] // Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2022 : 4543 - 4547 .

BAKSHY E , HOFMAN J M , MASON W A , et al . Everyone’s an influencer: quantifying influence on twitter [C ] // Proceedings of the Fourth ACM International Conference on Web Search and Data Mining . New York : ACM Press , 2011 : 65 - 74 .

TSUR O , RAPPOPORT A . What’s in a hashtag? Content based prediction of the spread of ideas in microblogging communities [C ] // Proceedings of the Fifth ACM International Conference on Web Search and Data Mining . New York : ACM Press , 2012 : 643 - 652 .

GAO S , MA J , CHEN Z M . Effective and effortless features for popularity prediction in microblogging network [C ] // Proceedings of the 23rd International Conference on World Wide Web . New York : ACM Press , 2014 : 269 - 270 .

RIZOIU M A , XIE L X , SANNER S , et al . Expecting to be HIP: hawkes intensity processes for social media popularity [C ] // Proceedings of the 26th International Conference on World Wide Web . New York : ACM Press , 2017 : 735 - 744 .

LI C , MA J Q , GUO X X , et al . DeepCas: an end-to-end predictor of information cascades [C ] // Proceedings of the 26th International Conference on World Wide Web . New York : ACM Press , 2017 : 577 - 586 .

SUN X G , ZHOU J Y , LIU L , et al . Explicit time embedding based cascade attention network for information popularity prediction [J ] . Information Processing & Management , 2023 , 60 ( 3 ): 103278 .

GAO X F , XU W Y , ZHANG Z X , et al . Cross-platform event popularity analysis via dynamic time warping and neural prediction [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 2 ): 1337 - 1350 .

QIAN Y , XU W , LIU X , et al . Popularity prediction for marketer-generated content: a text-guided attention neural network for multi-modal feature fusion [J ] . Information Processing & Management , 2022 , 59 : 102984 .

XU K L , LIN Z M , ZHAO J Q , et al . Multimodal deep learning for social media popularity prediction with attention mechanism [C ] // Proceedings of the 28th ACM International Conference on Multimedia . New York : ACM Press , 2020 : 4580 - 4584 .

CHANG B , XU T , LIU Q , et al . Study on information diffusion analysis in social networks and its applications [J ] . International Journal of Automation and Computing , 2018 , 15 ( 4 ): 377 - 401 .

GOLDENBERG J , LIBAI B . Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata [J ] . Academy of Marketing Science Review , 2001 , 9 ( 3 ): 1 - 18 .

LITTLESTONE N . Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm [J ] . Machine Learning , 1988 , 2 ( 4 ): 285 - 318 .

GOFFMAN W , NEWILL V A . Generalization of epidemic theory. An application to the transmission of ideas [J ] . Nature , 1964 , 204 : 225 - 228 .

LIU Y , DIAO S M , ZHU Y X , et al . SHIR competitive information diffusion model for online social media [J ] . Physica A: Statistical Mechanics and Its Applications , 2016 , 461 : 543 - 553 .

WANG Z S , XIA C Y , CHEN Z Q , et al . Epidemic propagation with positive and negative preventive information in multiplex networks [J ] . IEEE Transactions on Cybernetics , 2021 , 51 ( 3 ): 1454 - 1462 .

GONG Y C , WANG M , LIANG W , et al . UHIR: an effective information dissemination model of online social hypernetworks based on user and information attributes [J ] . Information Sciences , 2023 , 644 : 119284 .

YU Z H , LU S , WANG D , et al . Modeling and analysis of rumor propagation in social networks [J ] . Information Sciences: an International Journal , 2021 , 580 : 857 - 873 .

GUO H M , YAN X F . Dynamic modeling and simulation of rumor propagation based on the double refutation mechanism [J ] . Information Sciences: an International Journal , 2023 , 630 : 385 - 402 .

HE L , ZHU L H , ZHANG Z D . Turing instability induced by complex networks in a reaction-diffusion information propagation model [J ] . Information Sciences: an International Journal , 2021 , 578 : 762 - 794 .

MA X R , SHEN S L , ZHU L H . Complex dynamic analysis of a reaction-diffusion network information propagation model with non-smooth control [J ] . Information Sciences: an International Journal , 2023 , 622 : 1141 - 1161 .

LIU X Y , TANG T , HE D B . Double-layer network negative public opinion information propagation modeling based on continuous-time Markov chain [J ] . The Computer Journal , 2021 , 64 ( 9 ): 1315 - 1325 .

曹玖新 , 高庆清 , 夏蓉清 , 等 . 社交网络信息传播预测与特定信息抑制 [J ] . 计算机研究与发展 , 2021 , 58 ( 7 ): 1490 - 1503 .

CAO J X , GAO Q Q , XIA R Q , et al . Information propagation prediction and specific information suppression in social networks [J ] . Journal of Computer Research and Development , 2021 , 58 ( 7 ): 1490 - 1503 .

YANG L , LI Z W , GIUA A . Containment of rumor spread in complex social networks [J ] . Information Sciences: an International Journal , 2020 , 506 : 113 - 130 .

YAO Q P , SHI R S , ZHOU C , et al . Topic-aware social influence minimization [C ] // Proceedings of the 24th International Conference on World Wide Web . New York : ACM Press , 2015 : 139 - 140 .

WANG S Z , ZHAO X J , CHEN Y , et al . Negative influence minimizing by blocking nodes in social networks [C ] // Proceedings of the 17th AAAI Conference on Late-Breaking Developments in the Field of Artificial Intelligence . New York : ACM Press , 2013 : 134 - 136 .

YAN R D , LI D Y , WU W L , et al . Minimizing influence of rumors by blockers on social networks: algorithms and analysis [J ] . IEEE Transactions on Network Science and Engineering , 2020 , 7 ( 3 ): 1067 - 1078 .

SONG C G , HSU W , LEE M L . Node immunization over infectious period [C ] // Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . New York : ACM Press , 2015 : 831 - 840 .

WANG B , CHEN G , FU L Y , et al . DRIMUX: dynamic rumor influence minimization with user experience in social networks [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 10 ): 2168 - 2181 .

SHI Q H , WANG C , YE D S , et al . Adaptive influence blocking: minimizing the negative spread by observation-based policies [C ] // Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2019 : 1502 - 1513 .

ZHU J M , NI P K , WANG G Q , et al . Misinformation influence minimization problem based on group disbanded in social networks [J ] . Information Sciences: an International Journal , 2021 , 572 : 1 - 15 .

YANG L , MA Z Y , LI Z W , et al . Rumor containment by blocking nodes in social networks [J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2023 , 53 ( 7 ): 3990 - 4002 .

KIMURA M , SAITO K , MOTODA H . Minimizing the spread of contamination by blocking links in a network [C ] // Proceedings of the 23rd National Conference on Artificial intelligence . Palo Alto : AAAI Press , 2008 : 1175 - 1180 .

TONG H H , PRAKASH B A , ELIASSI-RAD T , et al . Gelling, and melting, large graphs by edge manipulation [C ] // Proceedings of the 21st ACM International Conference on Information and Knowledge Management . New York : ACM Press , 2012 : 245 - 254 .

KUHLMAN C J , TULI G , SWARUP S , et al . Blocking simple and complex contagion by edge removal [C ] // Proceedings of the 2013 IEEE 13th International Conference on Data Mining . Piscataway : IEEE Press , 2013 : 399 - 408 .

KHALIL E B , DILKINA B , SONG L . Scalable diffusion-aware optimization of network topology [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM Press , 2014 : 1226 - 1235 .

YAN R D , LI Y , WU W L , et al . Rumor blocking through online link deletion on social networks [J ] . ACM Transactions on Knowledge Discovery from Data , 2019 , 13 ( 2 ): 1 - 26 .

MEDYA S , SILVA A , SINGH A . Approximate algorithms for data-driven influence limitation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 6 ): 2641 - 2652 .

GUO J X , LI Y , WU W L . Targeted protection maximization in social networks [J ] . IEEE Transactions on Network Science and Engineering , 2019 , 7 ( 3 ): 1645 - 1655 .

HE X R , SONG G J , CHEN W , et al . Influence blocking maximization in social networks under the competitive linear threshold model [C ] // Proceedings of the 2012 SIAM International Conference on Data Mining . Philadelphia : Society for Industrial and Applied Mathematics , 2012 : 463 - 474 .

SAXENA A , HSU W , LEE M L , et al . Mitigating misinformation in online social network with top-k debunkers and evolving user opinions [C ] // Proceedings of the Web Conference 2020 . New York : ACM Press , 2020 : 363 - 370 .

GHOSHAL A K , DAS N , DAS S . Influence of community structure on misinformation containment in online social networks [J ] . Knowledge-Based Systems , 2021 , 213 : 106693 .

刘维 , 杜宁宁 , 陈崚 , 等 . CIC 模型下基于社区检测的谣言抑制最大化方法 [J ] . 南京大学学报(自然科学版) , 2023 , 59 ( 2 ): 282 - 294 .

LIU W , DU N N , CHEN L , et al . Rumor blocking maximization method based on community detection under the CIC model [J ] . Journal of Nanjing University (Natural Science) , 2023 , 59 ( 2 ): 282 - 294 .

Views

2215

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Overview of detection techniques for malicious social bots

Related Author

Rong LIU

Bo CHEN

Ling YU

Ya-shang LIU

Si-yuan CHEN

Related Institution

School of Computer Science and Technology,Nanjing Normal University

Jiangsu Provincial Key Laboratory for Numerical of Large Scale Complex System

AI问答

⁰