Veracity of Data: From Truth Discovery Computation Algorithms to Models of Misinformation Dynamics

Laure Berti-Équille · Javier Borge-Holthoefer

Dec 2015 · Morgan & Claypool Publishers

About this ebook

In the Web, a massive amount of user-generated contents are available through various channels (e.g., texts, tweets, Web tables, databases, multimedia-sharing platforms, etc.). Conflicting information, rumors, erroneous and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. This monograph gives an overview of fundamental issues and recent contributions for ascertaining the veracity of data in the era of Big Data. The text is organized into six chapters, focusing on structured data extracted from texts. Chapter One introduces the problem of ascertaining the veracity of data in a multi-source and evolving context. Issues related to information extraction are presented in chapter Two. It is followed by practical techniques for evaluating data source reputation and authoritativeness in Chapter Three, including a review of the main models and Bayesian approaches of trust management. Current truth discovery computation algorithms are presented in details in Chapter Four. The theoretical foundations and various approaches for modeling diffusion phenomenon of misinformation spreading in networked systems is studied in Chapter Five. Finally, truth discovery computation from extracted data in a dynamic context of misinformation propagation raises interesting challenges that are explored in Chapter Six. Supplementary material including source codes, datasets, and slides are offered online. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of fact-checking, truth discovery or rumor spreading.

About the author

Dr. Laure Berti-Équille is a Senior Scientist at Qatar Computing Research Institute (QCRI), Hamad bin Khalifa University, since 2013. Prior joining QCRI, Laure was a “Directeur de Recherche” at IRD, the French Institute of Research for Development (2011-2013), a tenured Associate Professor at University of Rennes 1 in France (2000-2010) and a visiting researcher at AT&T Labs-Research (NJ, USA) (2007-2009) when she received a Marie Curie fellowship of the European Commission (Grant FP6-MOIF-CT-2006-041000). Her research interests focus on developing novel data management and analytics techniques for truth discovery, anomaly detection, data fusion and data curation. She has published one monograph, several book chapters, and over 80 research papers in refereed journals and conferences. She has served in the organization and program committees of over 50 international conferences and workshops. She is an associate editor of the ACM Journal of Data and Information Quality (JDIQ).

Dr. Javier Borge-Holthoefer, currently a Scientist at the Qatar Computing Research Institute (QCRI), Hamad bin Khalifa University, received a Ph.D. in Computer Science from the Universitat Rovira i Virgili (URV) in Tarragona (Catalonia, Spain) in 2011. Founded on interdisciplinary Physics, his research is focused on complex systems ranging from Cognitive Dynamics to Social Networks. Among other appointments, he taught at the Department of Computer Science and Mathematics and at the Department of Psychology (both in URV). Before moving to Qatar in 2014, he was a member of the COSNET Lab and held a position as a post-doctoral fellow at the Institute for Biocomputation and Physics of Complex Systems (BIFI), which belongs to the University of Zaragoza (Spain). With more than 20 peer-reviewed articles, his work has been published (among others) in the EPJ Data Science, Physical Review E, PLoS One, Scientific Reports or Europhysics Letters. Dr. Borge-Holthoefer has also contributed three chapters in different books around the problem of disease and information spreading on complex networks.