Guest Editorial: Automated Big Data Analysis for Social Multimedia Network Environments

Abstract

Recently, with the rapid proliferation of various social network services, it has become very common for people to express their thoughts or opinions on various issues using brief comments. Popular issues include political events, new movies, commercial products, controversial bills and presidential candidates, to name a few. Even though such comments contain personal opinion or preference, they can collectively represent public opinions or trends. In practice, such public opinions or trends could provide very crucial information in many applications. For instance, it is quite important to know from user comments whether public opinion is positive or negative on a specific issue such as new regulation or policy. Another interesting example is to know in advance how big hit a new movie would be from user reviews or reactions on the movie trailer. However, to predict such thing is a very complicated task because there are so many factors that have influence on it. In many cases, such user comments are informal and sometimes unstructured, in that they contain many non-standard structures such as abbreviations, informal terms, emoticons, etc. Most previous works have focused on analyzing long documents such as blogs and hence they are not effective for very short and possibly informal SNS documents. Therefore, due to the abovementioned problems, the big data analysis using data mining and machine learning techniques should be considered in social network environment.We have finally selected nineteen manuscripts for this special issue after the first, second review processes. Each manuscript selected was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers.The paper entitled BMulti-Scale Local Structure Patterns Histogram for Describing Visual Contents in Social Image Retrieval Systems,^by Baik et al. [1] proposes a local descriptor for personalized social image collections by utilizing twenty distinct structure patterns in salient edge maps at multiple scales. This allows both fine-grained and coarse-grained features to be

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