University of Twente Student Theses


Filtering social media data streams

Tolle, T. (2017) Filtering social media data streams.

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Abstract:With the increased usage of social media services data streams have become of growing importance. However applications that access information from these data streams often use REST API’s that provide them with easy access to a pre-filtered set of the data they are interested in. Accessing the information in the “raw” data stream could give administrators more control and insight over their data. This paper researches the question: How can administrators be assisted in defining effective filters for social media data streams? The answer to this question information is used to create an application with similar ease of use as current applications relying on the REST API’s while giving the administrator the insight and control over their data that is lacking from these existing applications. Literature research and user needs analyses are performed in order to discover the requirements for such an application and based on this several features proposed that should assist users in defining effective filters for social media data streams. These features are “suggestions”, “insight into removed data” and “separation”. These features were then implemented in a prototype application which allowed an administrative user to filter a Twitter data stream and display the results. This application was evaluated with several participants in order to determine if these proposed features did indeed assist administrators in defining effective filters. The results of this evaluation indicate that these features are effective in their goal. Further research is necessary in order to determine whether these results are also true for other social media platforms or possibly unstructured user generated data streams in general.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Creative Technology BSc (50447)
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