2000 word essay on Social networks and Digital Media

This work is split into 2X 1000 word essays with separate tasks Coursework ONE: User profile crawling For this coursework, you must create and curate a new Twitter account. Your profile (i.e. who you follow) should focus in on any one topic you are interested in (e.g. tennis, graph theory, politics). Choose 10-20 users related to this topic to follow. Through this, you should aim to collect profile of these related followers for performing analysis. Specifically: You will collect profile information about your user account and tweets from your timeline using the twitter API. 2. You will also iterate through the follower graph to collect other users connected to you (e.g. people following you, and their followers). 3. You will write a short report (approximately 1,000 words) analysing the data you have collected from your account. 4. Your report should include analysis on your influence, e.g. indegree, outdegree, <RT>retweet count distribution, your social interaction from the <@>mention and etc. This should be compared against the influence of other profiles you have collected data about. 5. You are also encouraged to derive some conclusions about your social behaviour based on the statistics from your own profile. Supporting materials: 1. Twitter API: Kevin Makice, “Twitter API: Up and Running”, O'Reilly Media, March 2009 2. Twitter4j: online documentation [login to view URL] 3. Examples: [login to view URL] Coursework TWO: For this coursework, you will perform more extensive analysis on another dataset. Specifically: 1. You should select a new network dataset. This can be a public dataset (any network of a few hundred or thousand nodes). Here are websites which provide a collection ofnetworks to choose from: [login to view URL] &[login to view URL] 2. You will write a short report (approximately 1,000 words) analysing the data. The results should be commented and justified. It is not sufficient to simply list results; you should derive conclusions. 3. The analysis can be related to, but certainly not limited to (this will depends on yournetwork dataset): a. The key graph characteristics of the graph (e.g. degree distribution, diameter etc.) b. The characteristics of social behavior within a community c. The similarities and differences of social behavior between community d. The characteristics of important (central) nodes in the network e. and many more... 4. You are free to use any data analysis tool, e.g. R, Matlab, Gephi, and can even calculate measurements by yourselves. Supporting material 1. R and Data mining : Yanchang Zhao, Chap 10&11, [login to view URL] 2. R language: Computing for Data Analysis [login to view URL] 3. iGraph with R:[login to view URL]