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Exploring the effect of streamed social media data variations on social network analysis
Derek Weber1,2, Mehwish Nasim3,4,5,6, Lewis Mitchell6,7
1School of Computer Science, University of Adelaide, Adelaide, SA Australia.
Social Network Analysis and Mining
|July 12, 2021
Summary
Data collection tools significantly impact online social network analysis. Variations in collected data from online social networks (OSN) can alter research findings, affecting social network analysis results.
Area of Science:
- Multidisciplinary study integrating computer science, sociology, and information science.
- Focus on computational social science and network analysis methodologies.
Background:
- Reliable online social network (OSN) data is crucial for understanding real-world events.
- Data completeness and accurate network construction are vital for social network analysis.
Purpose of the Study:
- To investigate the impact of online social network activity on real-world offline events.
- To assess the reliability of OSN data for constructing social and information networks.
- To present measurement case studies on how data variations affect social network analyses.
Main Methods:
- Development of a systematic comparison methodology for OSN data.
- Application of the methodology to five pairs of parallel datasets collected from Twitter.
- Conducting four distinct case studies to analyze variations in collected data.
Main Results:
- Significant differences were observed in datasets collected using different tools.
- Data variations were found to substantially alter the outcomes of subsequent social network analyses.
- The choice of data collection tools directly influences the reliability of inferred social networks.
Conclusions:
- Researchers must be aware of the potential biases introduced by different data collection tools.
- Guidelines are provided for researchers collecting online data streams to infer social networks.
- Emphasizes the need for methodological rigor in online data collection for social network analysis.
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