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Presenter: Jason Jubinville
Supervisor:

Date: Tue, November 27, 2018
Time: 13:00:00 - 14:00:00
Place: ECS 660

ABSTRACT

Abstract:

High Volume social media data has become commonplace. This talk will present an evaluation of various techniques for intra-set clustering of social media data from an industry perspective. The research goal of this work was to establish methods for reducing the amount of redundant information  end users must review from standard social media searches. The conducted work also evaluated both clustering algorithms and string similarity measures for their effectiveness in clustering a selection of real-world topic and location-based social media searches. In addition, the algorithms and similarity measures were tested in scenarios based on common industry constraints including rate limits. The results were evaluated on real-world social media data sets via several practical measures to determine which techniques were most effective.