Computational trust assessment of open media data

Abstract

The explosion and newly found reliance on open media has enabled rapid situational assessment especially in austere, hard to reach environments (e.g., sites with natural disasters, places of political unrest). However, the data, while plentiful has left users wishing to turn it into insights or information with a key void: trust assessment. Herein lies the challenge the authors aim to address in this paper: automated, computational trust assessment of rapidly generated and disseminated information.

Trust and Big Data: A Roadmap for Research

“…Lukoianova and Rubin [18] focus on veracity as a critical quality factor and introduce a Big Data veracity index that combines the three dimensions of subjectivity, deception and implausibility. Belov et al [19] propose a system for automated trust assessment of online open media that is based on both the assessment of the source and the content. And Albanese [20] tries to quantify the trustworthiness of both the data source and the data items by developing a solution similar to Google's PageRank method.…”