Network Analysis is a method for studying communication and socio-technical networks within a formal organization. It is a quantitative descriptive technique for creating statistical and graphical models of the people, tasks, groups, knowledge and resources of organizational systems. It is based on social network theory and more specifically, dynamic network analysis -- based on a set of actors (such as individuals or organizations) and the dyadic ties between these actors (such as relationships, connections, or interactions). R-SHIEF'S approach to network analysis involves various data visualization and digital humanities techniques.
Fig. 1 Topic modeling finds highly collocated words and arranges them into groups. What you are seeing on the attached list are the 100 "topics" that make up the Egyptian Twitter traffic. The actual topics have more words than these, this only includes the top 20. One of the issues with topic modeling is assigning the correct number of topics, and 100 may be too many or too few for 600,000 tweets. We can also choose to eliminate certain terms, such as #Egypt. Another note: each Tweet is made up of several topics, such that we can look at how different topics come into prominence over time.
Fig. 2 This is what is known as a bipartite graph meaning that we are showing two types of nodes: tweets and Twitter users. A user is connected to all tweets they have sent and to all tweets directed at them (through the @username aspect of a tweet). Each image is an Ego Network of a particular user, showing the tweets that they have sent, any users connected to those tweets, and any tweets that those users have sent. Tweets are color-coded by timing such that the earliest tweets are lighter and the darker tweets are later (relative to the dataset of 600,000 tweets with #Egypt). The green tweets represents tweets in Arabic and the blue ones are in English.