Exploring the Veracity of Online Claims with BackDrop

Julien Leblay, Weiling Chen, Steven J. Lynden

Abstract: Using the Web to assess the validity of claims presents many challenges. Whether the data comes from social networks or established media outlets, individual or institutional data publishers, one has to deal with scale and heterogeneity, as well as with incomplete, imprecise and sometimes outright false information. All of these are closely studied issues. Yet in many situations, the claims under scrutiny, and the data itself, have some inherent context-dependency making them impossible to completely disprove, or evaluate through a simple (e.g. scalar) measure. While data models used on the Web typically deal with universal knowledge, we believe the time has come to put context, such as time or provenance, at the forefront and watch knowledge through multiple lenses. We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context. BackDrop comes in the form of a Web interface, in which users can interactively populate and annotate knowledge bases, and explore under which circumstances certain claims are more or less credible.