PageRank-based approach on ranking social events: A case study with Flickr

Exploring social events from Social Network Services (SNSs) (known as detecting events) has been studied in many researches because of its challenges. Most of researches focus on detecting events based on textual context. In this paper, we propose a novel framework using media data for not only systematically identifying events but also ranking these events. Firstly, we detect events from the photos textual annotations as well as visual features (e.g., timestamp, location); and then effectively identify events by considering the spreading effect of events in the spatio-temporal space. Secondly, we use these relationships among events (e.g., event spatial, temporal and content) for enhancing the precision of the algorithm. Finally, we rank events by analyzing relationships between them (e.g., locations, timestamps, tags) at different period of time. The experiments are conducted with two different approaches: (i) using a collected dataset (offline approach), and (ii) using a realtime dataset (online approach).