Trust filter for disease surveillance : Identity

Abstract

A flexible and extensible mobile application was delivered for evaluation and optimal inclusion of NextGen (Next Generation) data sources into biosurveillance for early detection, situational awareness and prediction. We present trust analysis of NextGen data sources to increase data confidence. One of the trust filters is the Identity filter, which helps us determine the degree of separation between the sender and the subject of a sentence. In this thesis, the author presents the definition of Identity. To help us distinguish different degrees of separation, the author uses relation distance along with a family tree to weight different relationships. Then the author compares a discriminative algorithm and a generative algorithm to calculate a user's Identity score. In the end, the author concludes that it is a good choice to apply a binary classification algorithm combined with a Natural Language Processing algorithm because of the trade-off between accuracy and runtime complexity.