research question

07. How do key personalities and space agencies interact within Twitter?

Click on a space agency or a agency’s CEO to highlight tweet connections Tweets analyzed in a time range that goes from: 20 September to 20 November (2 month) Most relevant Tweet CEO Agency @PaulGAllen Marillyn Hewson @richardbranson @WesternUnionCIO @elonmusk @JeffBezos @janwoerner @ulalaunch @DRogozin @torybruno Faith Ozmen @JimBridenstine @BoeingCEO @LockheedMartin @blueorigin @virgingalactic @Stratolaunch @OrbitalATK @SpaceX @roscosmos @SierraNevCorp @Boeing @NASA @esa
Description Protocol Data

Description

The net aim is to show how the collaboration between private companies and national space agencies transpire in a social media that is largely preferred by institutions members and venture companies key personalities. This Twitter scraping visualization displays how these actors are related. The tweets collected show the relevance of CEO’s, playing a more significant role compared to national agencies. That’s the case of SpaceX’ CEO Elon Musk, mostly reaching the same following of NASA on this platform. It’s interesting to notice how all actors are involved in a sort of “tag game”. Private companies tagging national agencies that answer them back, private companies tagging their competitors, CEO’s talking directly one to another, and so on. The information provided in this network concerns this particular communicational featured in the sector. By reporting some of the most interesting tweets, it’s seen how a large majority concern mutual congratulations for partnership, missions, discoveries, milestones & spaceflight achievements, while the sentiment of these tweets appears almost positive and proactive.

Protocol

To deliver this protocol, Twitter has been selected as the most suitable platform to fit the question. 12 amongst companies and national agencies have been selected, considering the mentioned actors in previous protocols. A very first manual scraping has been made to get the number of tweets, the following and the follower profiles. For what concerns the tools, Twint and Python have been combined to extract tweet contents in text format considering a bimestral time range, starting from September 20th. The .txt files have been manually scraped to spot entities and personalities tagged in tweets. All the results have been organized by columns in an Excel folder in order to match company, account link, tweet counter, following, followers, and the frequency cited ID. This last column matches reciprocal mentions and tags for each actor considered.

Data

Data Source: Twitter
Timestamp: 20/09/2018
View Data