Little Data, Big Stories: Taking the Pulse of Large-Scaled Events on Twitter

With the proliferation of Big Data, Social Science projects being developed, this work takes a step back to design research avenues that specifically look at smaller, real-time Social Science projects. Building on an already developed platform, called Dynamic Twitter Network Analysis (DTNA), we build out exploration into multiple world event types, which were captured in real-time and used smaller datasets to allow the user the ability to seek location and topic-specific data collections in parallel to events occurring. With these datasets, we first establish what could be learned during the event that mimics larger projects in the same domain. Secondly, we compare the events to help bring awareness to strategies that can evolve as specific events occur. The datasets examined are from a 24-hour period from specific locations of relevance with a focus on polarizing events. This includes: 1)Boston Marathon Bombing, 2) Sandy Hook Elementary Shooting, 3) Gezi Park Riots, 4) Hurricane Sandy, 5) Batkid, Make-a-Wish Foundation, 6) Brazil World Cup Protests, and 7) 2014 NBA Championship (Game 5). These networks will be analyzed both from social network analysis (SNA) and natural language processing (NLP) approaches (including sentiment analysis and part of speech tagging comparing personal pronoun use).