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Online Social Networks (OSNs) have witnessed a tremendous growth the last fewyears, becoming a platform for online users to communicate, exchange contentand even find employment. The emergence of OSNs has attracted researchers andanalysts and much data-driven research has been conducted. However, collectingdata-sets is non-trivial and sometimes it is difficult for data-sets to beshared between researchers. The main contribution of this paper is a frameworkcalled SONG (Social Network Write Generator) to generate synthetic traces ofwrite activity on OSNs. We build our framework based on a characterizationstudy of a large Twitter data-set and identifying the important factors thatneed to be accounted for. We show how one can generate traces with SONG andvalidate it by comparing against real data. We discuss how one can extend anduse SONG to explore different `what-if' scenarios. We build a Twitter cloneusing 16 machines and Cassandra. We then show by example the usefulness of SONGby stress-testing our implementation. We hope that SONG is used by researchersand analysts for their own work that involves write activity.

Explore what-if scenarios with SONG: Social Network Write Generator