Year: 2018
Type: dissertation
Abstract: This thesis evaluates various techniques for intra-set clustering of social media data from an industry perspective. The research goal was to establish methods for reducing the amount of redundant information an end user must review from a standard social media search. The research 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 industry constraints such as rate limits. The results were evaluated using several practical measures to determine which techniques were effective.
Author: Jason Jubinville
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2906601469