Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Mapping recent information behavior research: an analysis of co-authorship and cocitation networks

There has been an increase in research published on information behavior in recent years, and this has been accompanied by an increase in its diversity and interaction with other fields, particularly information retrieval (HR). The aims of this study are to determine which researchers have contributed to producing the current body of knowledge on this subject, and to describe its intellectual basis. A bibliometric and network analysis was applied to authorship and co-authorship as well as citation and co-citation. According to these analyses, there is a small number of authors who can be considered to be the most productive and who publish regularly, and a large number of transient ones. Other findings reveal a marked predominance of theoretical works, some examples of qualitative methodology that originate in other areas of social science, and a high incidence of research focused on the user interaction with information retrieval systems and the information behavior of doctors

Rutgers' TREC-7 Interactive Track Experience

We present results of a study comparing two different interactive information retrieval systems: one which supports positive relevance feedback as a termsuggestion device; the other which supports both positive and negative relevance feedback in this same context. The purpose of the study was to investigate the effectiveness and usability of a specific implementation of negative relevance feedback in interactive information retrieval. A second purpose was to investigate the effectiveness and usability of relevance feedback implemented as a term-suggestion device. The results suggest that, although there was no benefit in terms of performance for the system with negative and positive relevance feedback, this might be due to specific implementation issues. 1.0 Introduction As in TREC-7, we continued the work begun in our TREC-6 experiments (Belkin, et al., 1998), investigating the effectiveness and usability of negative relevance feedback (RF) in interactive information retrieval (IR)...