  Advances in information technology reduce barriers to information
propagation, but at the same time they also induce the information overload
problem. For the making of various decisions, mere digestion of the relevant
information has become a daunting task due to the massive amount of information
available. This information, such as that generated by evaluation systems
developed by various web sites, is in general useful but may be noisy and may
also contain biased entries. In this study, we establish a framework to
systematically tackle the challenging problem of information decoding in the
presence of massive and redundant data. When applied to a voting system, our
method simultaneously ranks the raters and the ratees using only the evaluation
data, consisting of an array of scores each of which represents the rating of a
ratee by a rater. Not only is our appraoch effective in decoding information,
it is also shown to be robust against various hypothetical types of noise as
well as intentional abuses.
