This article has modeled how to compute veracity of web event
via uncertainty based on its features distribution on different kind of
confident websites, whose data is big. Some web events have positive
effects through facilitating public supervision, while others have
negative effects, such as MeiLing Guo event. Our work throws light
on timely responding to and understanding popular/emergency web
events for individuals and social groups. We propose one method
to measure web event veracity through calculating web event uncertainty.
In the process of calculating web event veracity, the main
contributions of this paper are as follows:
Some event features, which influence on the measurement of veracity
in the evolution process of web event, are mined to calculate
the veracity of web event.
Relations of features play an important role in computing veracity
of web event. The relations include distribution of attributes on
webpages and classified webpages on websites. Moreover iterative
processes are employed to handle these relations.
Matrix operations are done to confirm that the result of the iteration
progress is in coincidence with mathematical model. Finally,
experiments are made based on the analysis above, and the results
prove that the iterative algorithm is promising.
In all, this paper analyzed web event from its event features to
the relation among these features. All the analysis contributes to the
computation of web event veracity.