基于多模态学习的虚假新闻检测研究

doi:10.3778/j.issn.1673-9418.2301064

摘要/Abstract

摘要： 社交媒体在给人们带来便利的同时，也成为虚假新闻恣意传播的渠道，如果不及时发现遏止，极易引发群众恐慌，激起社会动荡。因此，探索准确高效的虚假新闻检测技术具有极高的理论价值和现实意义。对虚假新闻相关检测技术做了全面综述。首先，对多模态虚假新闻的相关概念进行了整理和归纳，并分析了单模态和多模态新闻数据集的变化趋势。其次，介绍了基于机器学习和深度学习的单模态虚假新闻检测技术，这些技术在虚假新闻检测领域已被广泛应用，而由于虚假新闻通常包含多种数据表现形式，这些传统的单模态技术无法充分挖掘虚假新闻的深层逻辑，因此无法有效地应对多模态虚假新闻数据带来的挑战。针对此问题，对近些年来先进的多模态虚假新闻检测技术进行了整理，从多流架构和图架构的角度归纳和论述了这些多模态检测的技术方法，探讨了这些技术的思想理念与潜在缺陷。最后，分析了目前虚假新闻检测研究领域存在的困难和瓶颈，并由此给出未来的研究方向。

关键词: 虚假新闻检测, 多模态学习, 深度学习, 社交网络

Abstract: While social media brings convenience to people, it has also become a channel for the arbitrary spread of fake news. If not detected and stopped in time, it is easy to cause public panic and social unrest. Therefore, exploring accurate and efficient fake news detection technology has high theoretical value and practical significance. This paper provides a comprehensive overview of the related fake news detection techniques. Firstly, the relevant concepts of multi-modal fake news are sorted and summarized, and the trend of changes in single-modal and multi-modal news datasets is analyzed. Secondly, this paper introduces single-modal fake news detection techniques based on machine learning and deep learning, which have been widely used in the field of fake news detection. However, traditional single-modal techniques cannot fully explore the deep logic of fake news because fake news usually contains multiple data presentations. Thus, they are unable to effectively deal with the challenges brought by multi-modal fake news data. To address this issue, this paper summarizes and discusses advanced multi-modal fake news detection techniques from the perspectives of multi-stream and graph architectures, and explores their concepts and potential drawbacks. Finally, this paper analyzes the difficulties and bottlenecks in the current research on fake news detection and provides future research directions based on these analyses.

Key words: fake news detection, multi-model learning, deep learning, social network

刘华玲, 陈尚辉, 曹世杰, 朱建亮, 任青青. 基于多模态学习的虚假新闻检测研究[J]. 计算机科学与探索, 2023, 17(9): 2015-2029.

LIU Hualing, CHEN Shanghui, CAO Shijie, ZHU Jianliang, REN Qingqing. Survey of Fake News Detection with Multi-model Learning[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(9): 2015-2029.

参考文献

[1] NGADIRON S, ABD AZIZ A, MOHAMED S S. The spread of COVID-19 fake news on social media and its impact among Malaysians[J]. International Journal of Law, Government and Communication, 2021, 6(22): 253-260.
[2] SHU K, SLIVA A, WANG S, et al. Fake news detection on social media: a data mining perspective[J]. ACM SIGKDD Explorations Newsletter, 2017, 19(1): 22-36.
[3] MANZOOR S I, SINGLA J. Fake news detection using machine learning approaches: a systematic review[C]//Proceedings of the 3rd International Conference on Trends in Electronics and Informatics, Tirunelveli, Apr 23-25, 2019. Piscataway: IEEE, 2019: 230-234.
[4] MRIDHA M F, KEYA A J, HAMID M A, et al. A compre-hensive review on fake news detection with deep learning[J]. IEEE Access, 2021, 9: 156151-156170.
[5] KUMAR S, KUMAR S, YADAV P, et al. A survey on analysis of fake news detection techniques[C]//Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems, Coimbatore, Mar 25-27, 2021. Piscataway:IEEE, 2021: 894-899.
[6] ALAM F, CRESCI S, CHAKRABORTY T, et al. A survey on multimodal disinformation detection[J]. arXiv:2103.12541, 2021.
[7] GUIMAR?ES N, FIGUEIRA á, TORGO L. Can fake news detection models maintain the performance through time? A longitudinal evaluation of Twitter publications[J]. Mathe-matics, 2021, 9(22): 2988.
[8] ALI I, AYUB M N B, SHIVAKUMARA P, et al. Fake news detection techniques on social media: a survey[J]. Wireless Communications and Mobile Computing, 2022: 6072084.
[9] MA J, GAO W, MITRA P, et al. Detecting rumors from micro-blogs with recurrent neural networks[C]//Proceedings of the 25th International Joint Conference on Artificial Intel-ligence, New York, Jul 9-15, 2016. Menlo Park: AAAI, 2016: 3818-3824.
[10] CAO J, GUO J, LI X, et al. Automatic rumor detection on microblogs: a survey[J]. arXiv:1807.03505, 2018.
[11] BOIDIDOU C, PAPADOPOULOS S, ZAMPOGLOU M, et al. Detection and visualization of misleading content on Twitter[J]. International Journal of Multimedia Information Retrieval, 2018, 7(1): 71-86.
[12] PAPADOPOULOU O, ZAMPOGLOU M, PAPADOPOULOS S, et al. A corpus of debunked and verified user-generated videos[J]. Online Information Review, 2018, 43(1): 72-88.
[13] MA J, GAO W, WONG K F. Detect rumors in microblog posts using propagation structure via kernel learning[C]//Pro-ceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Jul 30-Aug 4, 2017. Stroudsburg: ACL, 2017: 708-717.
[14] DAI E, SUN Y, WANG S. Ginger cannot cure cancer: battling fake health news with a comprehensive data repository[C]//Proceedings of the 14th International AAAI Conference on Web and Social Media, Atlanta, Jun 8-11, 2020. Menlo Park: AAAI, 2020: 853-862.
[15] LI Y, JIANG B, SHU K, et al. MM-COVID: a multilingual and multimodal data repository for combating COVID-19 disinformation[J]. arXiv:2011.04088, 2020.
[16] ZUBIAGA A, LIAKATA M, PROCTER R. Learning repor-ting dynamics during breaking news for rumour detection in social media[J]. arXiv:1610.07363, 2016.
[17] SHU K, MAHUDESWARAN D, WANG S, et al. Fake-newsnet: a data repository with news content, social con-text, and spatiotemporal information for studying fake news on social media[J]. Big Data, 2020, 8(3): 171-188.
[18] 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 Lin-guistics, Vancouver, Jul 30-Aug 4, 2017. Stroudsburg: ACL, 2017: 422-426.
[19] RASHKIN H, CHOI E, JANG J Y, et al. Truth of varying shades: analyzing language in fake news and political fact-checking[C]//Proceedings of the 2017 Conference on Empi-rical Methods in Natural Language Processing, Copen-hagen, Sep 9-11, 2017. Stroudsburg: ACL, 2017: 2931-2937.
[20] AHMED H, TRAORE I, SAAD S. Detection of online fake news using n-gram analysis and machine learning techni-ques[C]//Proceedings of the 1st International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, Vancouver, Oct 26-28, 2017. Cham: Springer, 2017: 127-138.
[21] 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 13th International Conference on Web and Social Media, Münich, Jun 11-14, 2019. Menlo Park: AAAI, 2019: 573-582.
[22] SHAHI G K, DIRKSON A, MAJCHRZAK T A. An explo-ratory study of COVID-19 misinformation on Twitter[J]. On-line Social Networks and Media, 2021, 22: 100104.
[23] N?RREGAARD J, HORNE B D, ADALI S. NELA-GT-2018: a large multi-labelled news dataset for the study of misinformation in news articles[C]//Proceedings of the 13th International Conference on Web and Social Media, Münich, Jun 11-14, 2019. Menlo Park: AAAI, 2019: 630-638.
[24] GRUPPI M, HORNE B D, ADALI S. NELA-GT-2021: a large multi-labelled news dataset for the study of misinformation in news articles[J]. arXiv:2203.05659, 2022.
[25] 高玉君, 梁刚, 蒋方婷, 等. 社会网络谣言检测综述[J]. 电子学报, 2020, 48(7): 1421-1435.
GAO Y J, LIANG G, JIANG F T, et al. Social network rumor detection: a survey[J]. Acta Electronic Sinica, 2020, 48(7): 1421-1435.
[26] ZHOU X, ZAFARANI R. Network-based fake news detec-tion: a pattern-driven approach[J]. SIGKDD Explorations, 2019, 21(2): 48-60.
[27] ZHAO Y, ZOBEL J. Searching with style: authorship attribu-tion in classic literature[C]//Proceedings of the 30th Austra-lasian Computer Science Conference, Victoria, Jan 30-Feb 2, 2007. Sydney: Australian Computer Society, 2007: 59-68.
[28] FENG S, BANERJEE R, CHOI Y. Syntactic stylometry for deception detection[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, Jul 8-14, 2012. Stroudsburg: ACL, 2012: 171-175.
[29] PéREZ-ROSAS V, KLEINBERG B, LEFEVRE A, et al. Auto-matic detection of fake news[J]. arXiv:1708.07104, 2017.
[30] RUBIN V L, CONROY N J, CHEN Y. Towards news verifica-tion: deception detection methods for news discourse[C]//Proceedings of the 2015 Hawaii International Conference on System Sciences, Hawaii, Jan 5-8, 2015. Washington:IEEE Computer Society, 2015: 5-8.
[31] KARIMI H, TANG J. Learning hierarchical discourse-level structure for fake news detection[J]. arXiv:1903.07389, 2019.
[32] SHARMA K, QIAN F, JIANG H, et al. Combating fake news: a survey on identification and mitigation techniques[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(3): 1-42.
[33] 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 and Secure Compu-ting, 2012, 9(6): 811-824.
[34] CASTILLO C, MENDOZA M, POBLETE B. Information cre-dibility on Twitter[C]//Proceedings of the 20th International Conference on World Wide Web, Hyderabad, Mar 28-Apr 1, 2011. New York: ACM, 2011: 675-684.
[35] GUO H, CAO J, ZHANG Y, et al. Rumor detection with hierarchical social attention network[C]//Proceedings of the 27th ACM International Conference on Information and Know-ledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 943-951.
[36] 段大高, 盖新新, 韩忠明, 等. 基于梯度提升决策树的微博虚假消息检测[J]. 计算机应用, 2018, 38(2): 410-414.
DUAN D G, GAI X X, HAN Z M, et al. Micro-blog mis-information detection based on gradient boost decision tree[J]. Journal of Computer Applications, 2018, 38(2): 410-414.
[37] 张仰森, 彭媛媛, 段宇翔, 等. 基于评论异常度的新浪微博谣言识别方法[J]. 自动化学报, 2020, 46(8): 1689-1702.
ZHANG Y S, PENG Y Y, DUAN Y X, et al. The method of Sina Weibo rumor detecting based on comment abnormality[J]. Acta Automatica Sinica, 2020, 46(8): 1689-1702.
[38] MA J, GAO W, WEI Z, et al. Detect rumors using time series of social context information on microblogging websites[C]//Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Oct 18-23, 2015. New York: ACM, 2015: 1751-1754.
[39] BAHAD P, SAXENA P, KAMAL R. Fake news detection using bi-directional LSTM-recurrent neural network[J]. Procedia Computer Science, 2019, 165: 74-82.
[40] 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 the 2018 Pacific-Asia Con-ference on Trends and Applications in Knowledge Disco-very and Data Mining, Melbourne, Jun 3-8, 2018. Cham: Springer, 2018: 40-52.
[41] YU F, LIU Q, WU S, et al. A convolutional approach for misinformation identification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, Mel-bourne, Aug 19-25, 2017. Menlo Park: AAAI, 2017: 3901-3907.
[42] QIAN F, GONG C, SHARMA K, et al. Neural user response generator: fake news detection with collective user intelli-gence[C]//Proceedings of the 27th International Joint Con-ference on Artificial Intelligence, Stockholm, Jul 13-19, 2018. New York: AAAI Press, 2018: 3834-3840.
[43] MIKOLOV T, DEORAS A, KOMBRINK S, et al. Empirical evaluation and combination of advanced language modeling techniques[C]//Proceedings of the 12th Annual Conference of the International Speech Communication Association, Florence, Aug 27-31, 2011. Baixas: ISCA, 2011: 605-608.
[44] DUNGS S,?AKER A,?FUHR N,?et al. Can rumour stance alone predict veracity?[C]//Proceedings of the 27th International Conference on Computational Linguistics, New Mexico, Aug 20-26, 2018. Stroudsburg: ACL, 2018: 3360-3370.
[45] MA J, GAO W, WONG K F. Detect rumors on Twitter by promoting information campaigns with generative adver-sarial learning[C]//Proceedings of the 2019 World Wide Web Conference,?San Francisco, May 13-17, 2019. New York: ACM, 2019: 3049-3055.
[46] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with con-volutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, Jun 7-12, 2015. Washington: IEEE Computer Society, 2015: 1-9.
[47] 何韩森, 孙国梓. 基于特征聚合的假新闻内容检测模型[J]. 计算机应用, 2020, 40(8): 2189-2193.
HE H S, SUN G Z. Fake news content detection model based on feature aggregation[J]. Journal of Computer Appli-cations, 2020, 40(8): 2189-2193.
[48] 孙尉超, 陈涛. 基于ALBERT-BiLSTM模型的微博谣言识别方法研究[J]. 计算机时代, 2020(8): 21-26.
SUN W C, CHEN T. Research on Microblog rumor recogni-tion method based on ALBERT-BiLSTM model[J]. Compu-ter Era, 2020(8): 21-26.
[49] KALIYAR R K, GOSWAMI A, NARANG P. FakeBERT: fake news detection in social media with a BERT-based deep learning approach[J]. Multimedia Tools and Applications, 2021, 80(8): 11765-11788.
[50] MAYANK M, SHARMA S, SHARMA R. DEAP-FAKED: knowledge graph based approach for fake news detection[J]. arXiv:2107.10648, 2021.
[51] MORENCY L P, LIANG P P, ZADEH A. Tutorial on multi-modal machine learning[C]//Proceedings of the 2022 Con-ference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno-logies: Tutorial Abstracts, Seattle, Jul 10-15, 2022. Strouds-burg: ACL, 2022: 33-38.
[52] KIM W, SON B, KIM I. ViLT: vision-and-language trans-former without convolution or region supervision[C]//Pro-ceedings of the 38th International Conference on Machine Learning, Jul 18-24, 2021: 5583-5594.
[53] NAGRANI A, YANG S, ARNAB A, et al. Attention bottle-necks for multimodal fusion[C]//Advances in Neural Infor-mation?Processing?Systems?34,?Dec?6-14, 2021: 14200-14213.
[54] JIN Z, CAO J, GUO H, et al. Multimodal fusion with recurrent neural networks for rumor detection on microblogs[C]//Pro-ceedings of the 25th ACM International Conference on Multimedia, California, Oct 23-27, 2017. New York: ACM, 2017: 795-816.
[55] SINGHAL S, SHAH R R, CHAKRABORTY T, et al. Spot-Fake: a multi-modal framework for fake news detection[C]//Proceedings of the 2019 IEEE 5th International Con-ference on Multimedia Big Data, Singapore, Sep 11-13, 2019. Piscataway: IEEE, 2019: 39-47.
[56] SINGHAL S, KABRA A, SHARMA M, et al. SpotFake+: a multimodal framework for fake news detection via transfer learning (student abstract)[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 13915-13916.
[57] 陈志毅, 隋杰. 基于DeepFM和卷积神经网络的集成式多模态谣言检测方法[J]. 计算机科学, 2022, 49(1): 101-107.
CHEN Z Y, SUI J. DeepFM and convolutional neural networks ensembles for multimodal rumor detection[J]. Computer Science, 2022, 49(1): 101-107.
[58] SONG C, NING N, ZHANG Y, et al. A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks[J]. Information Processing & Management, 2021, 58(1): 102437.
[59] QIAN S, WANG J, HU J, et al. Hierarchical multi-modal contextual attention network for fake news detection[C]//Proceedings of the 44th International ACM SIGIR Con-ference on Research and Development in Information Ret-rieval, Jul 11-15, 2021. New York: ACM, 2021: 153-162.
[60] 亓鹏, 曹娟, 盛强. 语义增强的多模态虚假新闻检测[J]. 计算机研究与发展, 2021, 58(7): 1456-1465.
QI P, CAO J, SHENG Q. Semantics-enhanced multi-modal fake news detection[J]. Journal of Computer Research and Development, 2021, 58(7): 1456-1465.
[61] 戚力鑫, 万书振, 唐斌, 等. 基于注意力机制的多模态融合谣言检测方法[J]. 计算机工程与应用, 2022, 58(19): 209-217.
QI L X, WAN S Z, TANG B, et al. Multimodal fusion rumor detection method based on attention mechanism[J]. Compu-ter Engineering and Applications, 2022, 58(19): 209-217.
[62] ZHOU X, WU J, ZAFARANI R. SAFE: similarity-aware multi-modal fake news detection[C]//LNCS 12085: Procee-dings of the 24th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Singapore, May 11-14, 2020. Cham: Springer, 2020: 354-367.
[63] GIACHANOU A, ZHANG G, ROSSO P. Multimodal multi-image fake news detection[C]//Proceedings of the 2020 IEEE 7th International Conference on Data Science and Ad-vanced Analytics, Sydney, Oct 6-9, 2020. Piscataway: IEEE, 2020: 647-654.
[64] MüLLER-BUDACK E, THEINER J, DIERING S, et al. Multimodal analytics for real-world news using measures of cross-modal entity consistency[C]//Proceedings of the 2020 International Conference on Multimedia Retrieval, Dublin, Jun 8-11, 2020. New York: ACM, 2020: 16-25.
[65] XUE J, WANG Y, TIAN Y, et al. Detecting fake news by exploring the consistency of multimodal data[J]. Informa-tion Processing & Management, 2021, 58(5): 102610.
[66] CHEN Y, LI D, ZHANG P, et al. Cross-modal ambiguity learning for multimodal fake news detection[C]//Procee-dings of the 2022 ACM Web Conference, Lyon, Apr 25-29, 2022. New York: ACM, 2022: 2897-2905.
[67] WANG Y, MA F, JIN Z, et al. EANN: event adversarial neural networks for multi-modal fake news detection[C]//Procee-dings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, Aug 19-23, 2018. New York: ACM, 2018: 849-857.
[68] KHATTAR D, GOUD J S, GUPTA M, et al. MVAE: multi-modal variational autoencoder for fake news detection[C]//Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 2915-2921.
[69] QIAN S, HU J, FANG Q, et al. Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2021, 17(3): 1-23.
[70] ZHANG H, FANG Q, QIAN S, et al. Multi-modal knowledge-aware event memory network for social media rumor detec-tion[C]//Proceedings of the 27th ACM International Confe-rence on Multimedia, Nice, Oct 21-25, 2019. New York: ACM, 2019: 1942-1951.
[71] ASWANI R, KAR A K, ILAVARASAN P V. Experience: managing misinformation in social media—insights for policymakers from Twitter analytics[J]. Journal of Data and Information Quality, 2019, 12(1): 1-18.
[72] WU K, YANG S, ZHU K Q. False rumors detection on Sina Weibo by propagation structures[C]//Proceedings of the 2015 IEEE 31st International Conference on Data Enginee-ring, Seoul, Apr 13-17, 2015. Piscataway: IEEE, 2015: 651-662.
[73] 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 Compu-tational Linguistics, Melbourne, Jul 15-20, 2018. Stroudsburg: ACL, 2018: 1980-1989.
[74] DAVOUDI M, MOOSAVI M R, SADREDDINI M H. DSS: a hybrid deep model for fake news detection using propaga-tion tree and stance network[J]. Expert Systems with Appli-cations, 2022, 198: 116635.
[75] SHU K, WANG S, LIU H. Beyond news contents: the role of social context for fake news detection[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Feb 11-15, 2019. New York:ACM, 2019: 312-320.
[76] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J]. arXiv:1609.02907, 2016.
[77] DOU Y, LIU Z, SUN L, et al. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Ireland, Oct 19-23, 2020. New York: ACM, 2020: 315-324.
[78] CHANDRA S, MISHRA P, YANNAKOUDAKIS H, et al. Graph-based modeling of online communities for fake news detection[J]. arXiv:2008.06274, 2020.
[79] WANG Y, QIAN S, HU J, et al. Fake news detection via know-ledge-driven multimodal graph convolutional networks[C]//Proceedings of the 2020 International Conference on Multi-media Retrieval, Dublin, Jun 8-11, 2020. New York: ACM, 2020: 540-547.
[80] BIAN T, XIAO X, XU T, et al. Rumor detection on social media with bi-directional graph convolutional networks[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park: AAAI, 2020: 549-556.
[81] NI S, LI J, KAO H Y. MVAN: multi-view attention networks for fake news detection on social media[J]. IEEE Access, 2021, 9: 106907-106917.
[82] HU L, YANG T, ZHANG L, et al. Compare to the knowledge: graph neural fake news detection with external knowledge[C]//Proceedings of the 59th Annual Meeting of the Asso-ciation for Computational Linguistics and the 11th Interna-tional Joint Conference on Natural Language Processing, Aug 1-6, 2021. Stroudsburg: ACL, 2021: 754-763.
[83] NGUYEN V H, SUGIYAMA K, NAKOV P, et al. FANG: leveraging social context for fake news detection using graph representation[C]//Proceedings of the 29th ACM Inter-national Conference on Information & Knowledge Manage-ment, Ireland, Oct 19-23, 2020. New York: ACM, 2020: 1165-1174.
[84] SONG C, SHU K, WU B. Temporally evolving graph neural network for fake news detection[J]. Information Processing & Management, 2021, 58(6): 102712.
[85] CUI J, KIM K, NA S H, et al. Meta-path-based fake news detection leveraging multi-level social context information[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, Oct 17-21, 2022. New York: ACM, 2022: 325-334.