Event Detection from Social Data Stream Based on Time-Frequency Analysis

Social data have been emerged as a special big data resource of rich information, which is raw materials for diverse research to analyse a complex relationship network of users and huge amount of daily exchanged data packages on Social Network Services (SNS). The popularity of current SNS in human life opens a good challenge to discover meaningful knowledge from senseless data patterns. It is an important task in academic and business fields to understand user's behaviour, hobbies and viewpoints, but difficult research issue especially on a large volume of data. In this paper, we propose a method to extract real-world events from Social Data Stream using an approach in time-frequency domain to take advantage of digital processing methods. Consequently, this work is expected to significantly reduce the complexity of the social data and to improve the performance of event detection on big data resource.