Data journalism designates journalistic work inspired by digital data sources. A particularly popular and active area of data journalism is concerned with fact-checking. The term was born in the journalist community and referred the process of verifying and ensuring the accuracy of published media content; since 2012, however, it has increasingly focused on the analysis of politics, economy, science, and news content shared in any form, but first and foremost on the Web (social and otherwise). These trends have been noticed by computer scientists working in the industry and academia. Thus, a very lively area of digital content management research has taken up these problems and works to propose foundations (models), algorithms, and implement them through concrete tools. Our tutorial: (i) Outlines the current state of affairs in the area of digital (or computational) fact-checking in newsrooms, by journalists, NGO workers, scientists and IT companies; (ii) Shows which areas of digital content management research, in particular those relying on the Web, can be leveraged to help fact-checking, and gives a comprehensive survey of efforts in this area; (iii) Highlights ongoing trends, unsolved problems, and areas where we envision future scientific and practical advances.

In unstructured information retrieval P2P systems, semantic heterogeneity comes from the use of different ontologies. Semantic interoperability refers to the ability of peers to communicate with each others. We take into account these notions separately, as raising two different problems. Hence we propose two independent and complementary solutions. The GoOD-TA protocol aims at reducing heterogeneity through ontology-driven topology adaptation. DiQuESh is a top-k algorithm for distributed information retrieval that is intended to ensure interoperability. This distinction enables highlighting their respective benefits on the IR performances and leads to a modular architecture. For our experiments we obtained a set of actively used real-world ontologies through the NCBO BioPortal. We implemented GoOD-TA and DiQuESH in Java and used the PeerSim simulator. We first show that GoOD-TA nicely reduces the semantic heterogeneity related to the system topology, handles the evolution of peers' descriptors, and is suitable for dynamic systems. Then, GoOD-TA and DiQuESh are run simultaneously, with a significant increase of precision and recall. This enables to identify the indirect contribution of heterogeneity reduction obtained with GoOD-TA to improving interoperability.

We consider P2P data sharing systems in which each participant uses an ontology to represent information. If all the partipants do not use the same ontology, the system is said to be semantically heterogeneous. Several methods have been proposed to reach a degree of interoperability but thorough evaluation of these methods is prevented by a lack of tools to describe the situations in which they have been tested. In this paper we identify components that impact on the semantic heterogeneneity, and we define several complementary measures to capture the different facets of heterogeneity. Proposed measures allow to characterize the situation in which a method is evaluated, or to measure the heterogeneity reduction produced by another method.

We consider a distributed information system that allows autonomous consumers to query autonomous providers. We focus on the problem of query allocation from a new point of view, by considering consumers and providers' satisfaction in addition to query load. We define satisfaction as a long-run notion based on the consumers and providers' preferences. We propose and validate a mediation process, called SBMediation, which is compared to Capacity based query allocation. The experimental results show that SBMediation significantly outperforms Capacity based when confronted to autonomous participants.

We consider distributed information systems that are open, dynamic and provide access to large numbers of distributed, heterogeneous, autonomous information sources. Most of the work in data mediator systems has dealt with the problem of finding relevant information providers for a request. However, finding relevant requests for information providers is another important side of the mediation problem which has not received much attention. In this paper, we address these two sides of the problem with a flexible mediation process. Once the qualified information providers are identified, our process allows them to express their interest in a request via a bidding mechanism. It also requires to set up a requisition policy, because a request must always be answered if there are qualified providers. This work does not concern pure market mechanisms because we counter-balance the providers' bids by considering their quality wrt a request. We validate our process on a set of simulations in the context of load balancing, which is a good indicator of the system's overall performance. The results show that the mediation process provides a very good long-run regulation of the system, in particular when providers can leave the system. However, load balancing is not the natural application of the flexible mediation and additional testing is required to show the generality of the approach to non-depletable resources.

Bonom is a distributed agent based system to perform information retrieval on the Internet. The middle agents which perform the matching between site agents (information providers) and user agents (requesters) are numerous and can be world wide distributed. Moreover, the agents are structured into nested communities according to a hierarchy of information domains. This paper focuses on a Web site indexing process which uses disambiguated ontologies. It allows the site agent to know to which communities it has to subscribe to, in order to get requests it can accurately process.