CIUV: Collaborating Information Against Unreliable Views

TL;DR

This paper introduces CIUV, a novel method for truth-finding from conflicting information sources that accounts for malicious men's involvement, using a three-stage interactive approach with proven theoretical bounds and superior experimental performance.

Contribution

The paper proposes CIUV, a new three-stage method that incorporates men's involvement into truth-finding, addressing a gap in existing approaches.

Findings

CIUV achieves the smallest error compared to other methods.

Theoretical analysis provides bounds on CIUV's error.

Experimental results validate CIUV's effectiveness on real data.

Abstract

In many real world applications, the information of an object can be obtained from multiple sources. The sources may provide different point of views based on their own origin. As a consequence, conflicting pieces of information are inevitable, which gives rise to a crucial problem: how to find the truth from these conflicts. Many truth-finding methods have been proposed to resolve conflicts based on information trustworthy (i.e. more appearance means more trustworthy) as well as source reliability. However, the factor of men's involvement, i.e., information may be falsified by men with malicious intension, is more or less ignored in existing methods. Collaborating the possible relationship between information's origins and men's participation are still not studied in research. To deal with this challenge, we propose a method -- Collaborating Information against Unreliable Views (CIUV) --- in dealing with men's involvement for finding the truth. CIUV contains 3 stages for interactively mitigating the impact of unreliable views, and calculate the truth by weighting possible biases between sources. We theoretically analyze the error bound of CIUV, and conduct intensive experiments on real dataset for evaluation. The experimental results show that CIUV is feasible and has the smallest error compared with other methods.