【VLDB2019】虚假新闻（Fake News）检测全面综述教程，156页PPT带你进入这一领域

【导读】VLDB 2019(Very Large Data Bases)是三大国际顶尖数据库会议之一(其余二者为SIGMOD和ICDE)，根据大会官方公布，今年VLDB共接收了128篇Research Paper、22篇IndustryPaper和48个Demo。VLDB 2019已经于2019.8.26-2019.8.30在洛杉矶召开。专知推荐在VLDB 2019上的一个虚假新闻（Fake News）检测的全面综述教程，本教程是数据库和社交网络领域的三位资深教授讲授，提供了最新关于Fake News相关技术的全景视图，包括检测，传播，缓解和假新闻的干预，教程涵盖了数据集成、真相发现与融合、概率数据库、知识图和假新闻视角下的众包等领域的研究，也是一个新兴研究方向。

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内容简介

虚假新闻（Fake News）是对全球民主的一大威胁，导致人们对政府、新闻和社会的信任度下降。社交媒体和社交网络的大众普及，导致了假新闻的蔓延，阴谋论、虚假信息和极端观点在这些地方盛行。假新闻的发现与治理是当今时代的基本问题之一，受到了广泛的关注。尽管snopes、politifact等事实核查网站，以及谷歌、Facebook和Twitter等大公司，已经采取了初步措施来处理假新闻，但还有很多工作要做。作为一个跨学科的话题，从机器学习、数据库、新闻、政治学到其他很多领域，人们都在研究假新闻的各个方面。

 

本教程的目标有两个方面。首先，我们希望使数据库社区熟悉其他社区在打击假新闻方面所作的努力。我们提供有关各方面研究的最新技术的全景视图，包括检测，传播，缓解和假新闻的干预。

接下来，我们将简要而直观地总结数据库社区之前的研究，并讨论如何使用这些研究来抵消假新闻。

本教程涵盖了数据集成、真相发现与融合、概率数据库、知识图和假新闻视角下的众包等领域的研究。只有利用数据库和其他研究社区之间的协作关系，才能建立有效的工具来处理假新闻。我们希望我们的教程能够推动这种思想的综合和新思想的创造。

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讲者

Laks V.S. Lakshmanan是英属哥伦比亚大学计算机科学系的教授。他是BC高级系统研究所的研究员，并于2016年11月被任命为ACM杰出科学家。他的研究兴趣涉及数据库系统和相关领域的广泛主题，包括:关系数据库和面向对象数据库, 高级数据模型新颖的应用程序,OLAP和数据仓库、数据挖掘、数据集成、半结构化数据和XML，社交网络和社交媒体，推荐系统和个性化。

 

Michael Simpson是英属哥伦比亚大学计算机科学系的博士后研究员。他在维多利亚大学获得博士学位。他的研究兴趣包括数据挖掘，社会网络分析，以及图形问题的可扩展算法的设计。

 

Saravanan（Sara）Thirumuruganathan是HBKU QCRI数据分析小组的科学家。他在德克萨斯大学阿灵顿分校获得博士学位。他对数据集成/清洗和用于数据管理的机器学习非常感兴趣。Saravanan的作品被选为VLDB 2018/2012年度最佳论文，并获得了SIGMOD 2018年度研究重点奖。

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相关文献

Fake News Primer

References

• Lui Guo and Chris Vargo, “Fake News” and Emerging Online Media Ecosystem: An Integrated Intermedia Agenda-Setting Analysis of the 2016 U.S. Presidential Election. Communications Research, June 2018.

• Wu, Agrawal, Li, Yang, and Yu. Computational Fact-Checking through Query Perturbations. ACM TODS 2017.

Propagation of Fake News

References

• David Kempe, Jon Kleinberg, and Eva Tardos. Maximizing the spread of influence through a social network. KDD 2003.

• Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Wei Chen, and Yajun Wang. Prominent features of rumor propagation in online social media. ICDM 2013.

• Soroush Vosoughi, Deb Roy and Sinan Aral. The spread of true and false news online. Science 2018.

• Xinyi Zhou, Reza Zafarani. Fake News: A Survey of Research, Detection Methods, and Opportunities. arXiv preprint. 2018.

Detection of Fake News

References (Data Integration, Truth Discovery & Fusion)

• Jing Gao, Qi Li, Bo Zhao, Wei Fan, and Jiawei Han. Truth discovery and crowdsourcing aggregation: A unified perspective. PVLDB 2015.

• Yannis Katsis, Yannis Papakonstantinou. View-based data integration. Encyclopedia of Database Systems. 2009.

• Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya Parameswaran, and Christopher Ré. Slimfast: Guaranteed results for data fusion and source reliability. SIGMOD 2017.

• Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Kevin Murphy, Shaohua Sun, and Wei Zhang. From data fusion to knowledge fusion. PVLDB 2014.

• Xin Luna Dong, Laure Berti-Equille, and Divesh Srivastava. Integrating conflicting data: the role of source dependence. PVLDB 2009.

References (ML-based Detection)

• Subhabrata Mukherjee and Gerhard Weikum. Leveraging Joint Interactions for Credibility Analysis in News Communities. CIKM 2015.

• SVN Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt. Graph kernels. JMLR 2010.

• Xinyi Zhou, Reza Zafarani, Kai Shu, and Huan Liu. Fake news: Fundamental theories, detection strategies and challenges. WSDM 2019.

• Xinyi Zhou, Reza Zafarani. Fake News: A Survey of Research, Detection Methods, and Opportunities. arXiv preprint. 2018.

• Ke Wu, Song Yang, and Kenny Q. Zhu. False rumors detection on sina weibo by propagation structures." ICDE 2015.

References (Knowledge Graph-based Approaches )

• Akrami, Farahnaz, et al. "Re-evaluating Embedding-Based Knowledge Graph Completion Methods." Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018.

• Bordes, Antoine, et al. "Translating embeddings for modeling multi-relational data." Advances in neural information processing systems. 2013.

• Chang, Lijun, et al. "Optimal enumeration: Efficient top-k tree matching." Proceedings of the VLDB Endowment 8.5 (2015): 533-544.

• Cheng, Jiefeng, Xianggang Zeng, and Jeffrey Xu Yu. "Top-k graph pattern matching over large graphs." 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE, 2013.

• Ciampaglia, Giovanni Luca, et al. "Computational fact checking from knowledge networks." PloS one 10.6 (2015): e0128193

• Hamilton, Will, et al. "Embedding logical queries on knowledge graphs." Advances in Neural Information Processing Systems. 2018.

• Jeh, Glen and Jennifer Widom. SimRank: a measure of structural-context similarity. KDD (2002).

• Kazemi, Seyed Mehran, and David Poole. "Simple embedding for link prediction in knowledge graphs." Advances in Neural Information Processing Systems. 2018.

• Lao, Ni, and William W. Cohen. "Relational retrieval using a combination of path-constrained random walks." Machine learning 81.1 (2010): 53-67.

• Lin, Peng, et al. "Discovering graph patterns for fact checking in knowledge graphs." International Conference on Database Systems for Advanced Applications. Springer, Cham, 2018.

• Lin, Yankai, et al. "Learning entity and relation embeddings for knowledge graph completion." Twenty-ninth AAAI conference on artificial intelligence. 2015.

• Lü, Linyuan, Ci-Hang Jin, and Tao Zhou. "Similarity index based on local paths for link prediction of complex networks." Physical Review E 80.4 (2009): 046122.

• Morales, Camilo, et al. "MateTee: A semantic similarity metric based on translation embeddings for knowledge graphs." International Conference on Web Engineering. Springer, Cham, 2017.

• B. Shi and T. Weninger. Discriminative predicate path mining for fact checking in knowledge graphs. Knowledge-Based Sys., 104:123–133, 2016.

• Shi, Baoxu, and Tim Weninger. "ProjE: Embedding projection for knowledge graph completion." Thirty-First AAAI Conference on Artificial Intelligence. 2017.

• P. Shiralkar, A. Flammini, F. Menczer, and G. L. Ciampaglia. Finding streams in knowledge graphs to support fact checking. In 2017 IEEE ICDM 2017, pp 859–864, 2017.

• Wang, Zhen, et al. "Knowledge graph embedding by translating on hyperplanes." Twenty-Eighth AAAI conference on artificial intelligence. 2014.

• Xu, Zhongqi, Cunlai Pu, and Jian Yang. "Link prediction based on path entropy." Physica A: Statistical Mechanics and its Applications 456 (2016): 294-301.

• Yang, Bishan, et al. "Embedding entities and relations for learning and inference in knowledge bases." arXiv preprint arXiv:1412.6575 (2014).

• Yang, Shengqi, et al. "Schemaless and structureless graph querying." Proceedings of the VLDB Endowment 7.7 (2014): 565-576.

• Yang, Shengqi, et al. "Fast top-k search in knowledge graphs." 2016 IEEE 32nd international conference on data engineering (ICDE). IEEE, 2016.

Mitigation and Intervention of Fake News

References

• Bettencourt, Luís MA, et al. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models." Physica A: Statistical Mechanics and its Applications 364 (2006): 513-536.

• Bharathi, Shishir, David Kempe, and Mahyar Salek. "Competitive influence maximization in social networks." International workshop on web and internet economics. Springer, Berlin, Heidelberg, 2007.

• Budak, Ceren, Divyakant Agrawal, and Amr El Abbadi. "Limiting the spread of misinformation in social networks." Proceedings of the 20th international conference on World wide web. ACM, 2011.

• Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence through a social network." Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003.

• Khalil, Elias Boutros, Bistra Dilkina, and Le Song. "Scalable diffusion-aware optimization of network topology." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

• Konstantinou, Loukas, Ana Caraban, and Evangelos Karapanos. "Combating Misinformation Through Nudging.” Co-Inform Project. 2019.

• Medya, Sourav, Arlei Silva, and Ambuj Singh. "Influence Minimization Under Budget and Matroid Constraints: Extended Version." arXiv preprint arXiv:1901.02156 (2019).

• Nguyen, Nam P., et al. "Containment of misinformation spread in online social networks." Proceedings of the 4th Annual ACM Web Science Conference. ACM, 2012.

• Prakash, B. Aditya, et al. "Threshold conditions for arbitrary cascade models on arbitrary networks." Knowledge and information systems 33.3 (2012): 549-575.

• Prakash, B. Aditya, et al. "Fractional immunization in networks." Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2013.

• Tong, Guangmo, et al. "An efficient randomized algorithm for rumor blocking in online social networks." IEEE Transactions on Network Science and Engineering (2017).

• Tong, Hanghang, et al. "On the vulnerability of large graphs." 2010 IEEE International Conference on Data Mining. IEEE, 2010.

• Vo, Nguyen, and Kyumin Lee. "The rise of guardians: Fact-checking url recommendation to combat fake news." The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 2018

• Zhang, Yao, and B. Aditya Prakash. "Dava: Distributing vaccines over networks under prior information." Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014.

• Zhang, Yao, and B. Aditya Prakash. "Data-aware vaccine allocation over large networks." ACM Transactions on Knowledge Discovery from Data (TKDD) 10.2 (2015): 20.

• Zhao, Laijun, et al. "SIHR rumor spreading model in social networks." Physica A: Statistical Mechanics and its Applications 391.7 (2012): 2444-2453.

Future Opportunities

References

• Lucas Graves. Understanding the promise and limits of automated fact-checking. Factsheet 2018.

• Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan et al. ClaimBuster: the first-ever end-to-end fact-checking system. PVLDB 2017.

• Rene Speck, Diego Esteves, Jens Lehmann, and Axel-Cyrille Ngonga Ngomo. Defacto-a multilingual fact validation interface. ISWC 2015.

原文链接：

https://combatingfakenewstutorial.github.io/vldb19.html

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报告内容

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