Time-Frequency Social Data Analytics for Understanding Social Big Data
Social Network Services (SNS) have been the most popular channel where users can generate and disseminate a large amount of information (so-called ‘social big data’) among other users efficiently. Discovering meaningful patterns from these SNS (e.g., clustering relevant messages, detecting events, and understanding trends of social communities) is an important, but difficult research issue on social big data analytics. In this paper, we present an on-going work to transform social data in time domain to in frequency domain for detecting meaningful events from the social big data. Consequently, this work is expected to significantly reduce the volume (and also, complexity) of the social data and to improve the performance of the data analytics.
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