Power of Tags: Predicting Popularity of Social Media in Geo-Spatial and Temporal Contexts

Generating multimedia content and sharing them in social networks has become one of our daily-life activities. Although a lot of people care about the quality of the content itself, much less attention is paid to the text annotations. In our previous work, we have shown that the popularity of the content in social media is strongly affected by its annotated tags, and we have proposed a TF-IDF-like algorithm to analyze which tags are more potentially important to earn more popularity. In this paper, we extend the idea to show how the important tags are geo-spatially varied and how the importance ranking of the tags evolves over time.