Combating Fake News: A Data Management and Mining Perspective
Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more. The objective of this tutorial is two fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state of the art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones. PVLDB Reference Format: Laks V.S. Lakshmanan, Michael Simpson, Saravanan Thirumuruganathan. Combating Fake News: A Data Management and Mining Perspective. PVLDB, 12(12) 1990 1993, 2019. DOI: https:/doi.org/10.14778/3352063.3352117 This work is licensed under the Creative Commons AttributionNonCommercial NoDerivatives 4.0 International License. To view a copy of this license, visit http:/creativecommons.org/licenses/by nc nd/4.0/ For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s) Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 12, No. 12 ISSN 2150 8097. DOI: https:/doi.org/10.14778/3352063.3352117 1. MOTIVATION Fake news is one of the major problems facing governments, society, academia, and industry. The dissemination of false and misleading information has a corrosive effect on the trust of public toward various institutions [1] While fake news has always existed in history, the advent of new technology has exacerbated its reach and potential for damage. Social media and online social networks have allowed weaponization of fake news at an unprecedented scale [15, 30] Not surprisingly, the academic community has mobilized to counteract this phenomenon. Many research communities including databases, machine learning, data mining, journalism and political science have focused on different aspects of this problem. Our tutorial provides a unifying framework for categorizing prior research focusing on four facets of fake news: detection, propagation, mitigation and intervention. The work on detection seeks to identify which items are fake through diverse tools such as machine learning, content analysis, propagation analysis, computational fact checking and so on. The study of propagation seeks to understand and model how fake news spreads in various media such as social media. Once the fake news has spread, there are many techniques for addressing it. The mitigation based approaches seek to minimize the severity by "inoculating" users from falling for fake news, reduce the impact of filter bubbles, showing diverse viewpoints and so on. Finally, the intervention based approaches take an active role in reducing the spread of fake news. They could involve mild interventions such as amplifying real news alongside fake news to severe ones such as removing content or accounts. Goal of Tutorial. The goal of this tutorial is to provide an intuitive summary of research from other communities and discuss how prior work in the database community could be used to enrich them. Instead of delineating database and non database research, we offer a synthesis based approach that interleaves non database and database research that emphasizes the strong connection between them. We review various technical challenges, recent solutions and highlight a number of intriguing open problems at the intersection of various communities. We hope that this will empower database researchers to make impactful contributions in the fight against fake news. Scope of Tutorial. Given the huge amount of prior research on fake news and the limited amount of time, this tutorial provides a carefully selected subset of topics that we believe are most relevant to database researchers. For
Proc. VLDB Endow.