Automated Twitter data collecting tool and case study with rule-based analysis

Authors:
Changhyun Byun;Yanggon Kim;Hyeoncheol Lee;Kwangmi Ko Kim
Affiliations:
Towson University, Towson, MD;Towson University, Towson, MD;Towson University, Towson, MD;Towson University, Towson, MD
Venue:
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Year:
2012

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Abstract

Applying data mining techniques to social media can yield interesting perspectives about individual human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to build your own data set to apply data mining techniques without an automated data gathering and filtering system because of main characteristics of social media: the data is large, noisy and dynamic. To overcome these challenges, we developed a java-based data gathering tool that continually collects social data from Twitter and filters noisy data. This allows us, as well as other researchers, to build our own Twitter database. In this paper, we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. In addition, we provide an analysis of Twitter messages about various Super Bowl ads by applying data-mining techniques to a case study.