  In this study, we deal with the problem of judging the credibility of movie
reviews. The problem is challenging because even experts cannot clearly and
efficiently judge the credibility of a movie review and the number of movie
reviews is very large. To tackle this problem, we propose historical
credibility that judges the credibility of reviews based on the historical
ratings and textual reviews written by each reviewer. For this, we present
three kinds of criteria that can clearly classify the reviews into trusted or
distrusted ones. We validate the effectiveness of the proposed historical
credibility through extensive analysis. Specifically, we show that
characteristics between the trusted or distrusted reviews are quite
distinguishable in terms of three viewpoints: 1) distribution, 2) statistics,
and 3) correlation. Then, we apply historical credibility to a weakly
supervised model to classify a given review as a trusted or distrusted one.
First, we show that it is significantly efficient because the entire data set
is annotated according to the predefined criteria. Indeed, it can annotate
6,400 movie reviews only in 0.093 seconds, which occupy only 0.55%~1.88% of the
total learning time when we use LSTM and SVM as the learning model. Second, we
show that the historical credibility-based classification model clearly
outperforms the textual review-based classification model. Specifically, the
classification accuracy of the former outperforms that of the latter by up to
11.7%~13.4%. In addition, we clearly confirm that our classification model
shows higher accuracy as the data size increases.
