Analyzing behavior via a social network like Twitter is an increasingly popular technique for understanding social dynamics in real time. Twitter is extremely simple in nature, allowing users to place brief, text-only expressions online via ‘status updates’ or ‘tweets’ that are no more than 140 characters in length. Twitter's framing tends to yield in-the-moment expressions that reflect users' current experiences, making the service an ideal input signal for a real time societal hedonometer. The goal of this project is to extract emoticons such as “:)” and “:(” from tweets and to visualize the spread of emotions in the Twitter network by plotting the emoticons on a map in real time. The mapped Twitter data network is compared to a type of contact network, such as a disease network, to ascertain correlations. Contact networks follow general mathematical patterns. Node.js handled the streaming data and Socket.io, a javascript library for real time web applications, processed the data extraction. These technologies facilitated real time data extraction and display.
