Cognitive exploration of regions through analyzing geo-tagged social media data

Social media has now become a pervasive global communication channel. Many applications and platforms have become available for users to post messages, follow friends and share experiences. Due to the high frequency with which users update their states, a large amount of data is being generated around the world every second. By analyzing this data, valuable patterns can be extracted such as the distribution of users, their common interests, activities, locations visited, etc. In this paper, we focus on the cognitive exploration of photo sharing data. Traditionally, each photo sharing record comes with information about the location where the photo was taken, a timestamp, and potentially some description about the photo. Therefore, we can often deduce the features of photo-spots. Spots with similar features constitute a region of cognitive interest. The primary goal of this paper is to identify these regions and allow users to explore into regions of interest by cognitive understanding of their features and patterns of feature propagation in time. To achieve this goal, we propose an approach that makes use of semantic analysis in big data sets, data clustering, and cognitive visualization design issues. Our contributions are two-fold. First, we put forward a novel social-media data classification method. This is based on cognitive semantic analysis. Second, we suggest a new method to explore social activity maps by discovering regions of cognitive interest. In this paper, we introduce the design of an interactive visualization interface which projects photo sharing data to cognitive social activity map components. Experiments are performed on the Flickr dataset.