Net Neutrality debate has been amplified a lot across social media, using the dedicated hashtag #netneutrality. In order to understand what people discussed about, what terms have been used and by who, we chose to observe twitter for two weeks—from Nov 25th to Dec 7th 2014—collecting all tweets containing the hashtag #netneutrality. Data have been obtained with the TCAT tool. 47852 tweets have been analyzed, with 7520 users involved (authors and\or mentions).
Twitter Data have been downloaded from the TCAT tool, including number of mentions, tweets and followers of every user who tweeted #netneutrality in the two weeks analyzed. Since many users have been mentioned, but didn’t post any tweet (i.e.: @BarackObama, @TomWheelerFCC), the missing number of followers data have been filled using Twitter APIs. Due to the very strict limits of the APIs, most of the task has been completed by manually checking the number of followers via Twitter Website. Bot accounts have been excluded from the final visualization in order to easily compare data.
TCAT tool features a Bipartite hashtag-user graph, based on co-occurence of hashtags and users. Since the high number of users and sub-topics, twitter users have been divided into three sub-groups basing on their activity and influence (see below). For this reason the initial graph has been divided into three sub-graphs, by filtering the nodes and edges in Gephi.
To find out the most shared contents via Twitter, we finally focused on the tweets containing URLs, corresponding to the 23% of the total tweets. Links have been extracted using regular expressions matching. The most 30 shared short URLs (invalid links have been not included) have been expanded using URLEX.org, resulting in a list of 21 unique links. Then we chose a qualitative approach to analyze the content of each URL, in order to classify them at a later stage.
