【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- 专知

【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。

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。

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。

相关文献

Fake News Primer

References

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Propagation of Fake News

References

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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.
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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.
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References (Knowledge Graph-based Approaches )

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Mitigation and Intervention of Fake News

References

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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