  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.
