Thesis
NetNet, a tool for simplifying the workflow of analyzing social networks with textual content
Washington State University
Master of Science (MS), Washington State University
12/2020
DOI:
https://doi.org/10.7273/000004207
Handle:
https://hdl.handle.net/2376/124936
Abstract
Today's online social networks produce a significant amount of data that contain rich information. A key challenge is to analyze and make sense of the data. In many application scenarios, this requires analyzing both network topology information and textual content contained in the network. However, existing network analysis tools usually focus on one of these aspects, instead of providing end-to-end solutions for this particular research scenario. Therefore, users often need to utilize several different frameworks/tools with a complex workflow. In this thesis, we present NetNet, a social network analysis tool that is specifically designed to simplify the workflow of analyzing social networks containing both complicated network structure and massive textual information. In NetNet, we model social networks as interconnected user nodes with text nodes associated with them and leverage network analysis and text mining algorithms to seamlessly perform both tasks. In addition, our design utilizes web technologies to bundle the complicated workflow of data importing, network analysis, text analysis, and result delivery with a simple and efficient user interface. We evaluate the performance of our design with multiple sets of experiments on five datasets. The result shows that our design is practically efficient and scalable. We also perform a case study with NetNet to demonstrate how the workflow of analyzing social networks with textual contents is simplified.
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Details
- Title
- NetNet, a tool for simplifying the workflow of analyzing social networks with textual content
- Creators
- Jun Hao
- Contributors
- Xinghui Zhao (Advisor) - Washington State University, Engineering and Computer Science (VANC), School of
- Awarding Institution
- Washington State University
- Academic Unit
- Engineering and Computer Science (VANC), School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900896437301842
- Language
- English
- Resource Type
- Thesis