Exploring the effect of streamed social media data variations on social network analysis

TL;DR

This study investigates how variations in streamed social media data, specifically from Twitter, impact the construction and analysis of social networks, highlighting the importance of data collection methods.

Contribution

It introduces a systematic comparison methodology for assessing how data collection variations affect social network analysis from OSN data.

Findings

Significant differences found between datasets collected with different tools.

Data variations substantially alter social network analysis results.

Guidelines provided for researchers on data collection practices.

Abstract

To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affects social network analyses. To this end we developed a systematic com parison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.