Scaling up truth discovery

Abstract

The evolution of the Web from a technology platform to a social ecosystem has resulted in unprecedented data volumes being continuously generated, exchanged, and consumed. User-generated content on the Web is massive, highly dynamic, and characterized by a combination of factual data and opinion data. False information, rumors, and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. Truth discovery (also known as fact-checking) has recently gained lot of interest from Data Science communities. This tutorial will attempt to cover recent work on truth-finding and how it can scale Big Data. We will provide a broad overview with new insights, highlighting the progress made on truth discovery from information extraction, data and knowledge fusion, as well as modeling of misinformation dynamics in social networks. We will review in details current models, algorithms, and techniques proposed by various research communities whose contributions converge towards the same goal of estimating the veracity of data in a dynamic world. Our aim is to bridge theory and practice and introduce recent work from diverse disciplines to database people to be better equipped for addressing the challenges of truth discovery in Big Data.