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Prof Huan Liu
Arizona State University, USA |
| Abstract | |
| The pervasive use of social media such as Facebook, LinkedIn, Instagram, and Twitter has been producing great amounts of a new type of data, which is mostly user-generated, informal, incomplete, noisy, multi-media, and connected. It is also often accompanied with temporal and spatial information. It is an exceptionally rich source of data that allows us to study and understand human behavior and activities in new ways and unprecedented scales. It is undoubtedly big in scale, comes fast in speed, and spreads widely in an increasingly “flat” world. We will introduce these challenges and present some recent research issues we encounter: a big-data paradox unique to social media where many social networking sites are present but only minimum information is available, and an evaluation dilemma how we assess many results of social media mining without typical training-test data. We will exemplify the intricacies of social media data, and show how to exploit unique characteristics of social media data in developing novel algorithms and tools for social media mining. | |