Here below a guest post on Data Driven Journalism, WHERE JOURNALISM MEETS DATA. Follow the link to read the full article.
It’s been a very long time since the historic first Tweet “just setting up my twttr”, sent by founder Jack Dorsey, in 2006. In more than 10 years, Twitter has not become the most popular social media platform, yet it still remains an interesting and unique platform to analyze for three main reasons:
- Organized by interests: Differently from Facebook, where most people follow people they’ve met, Twitter is organized around interest graphs. Connections exist between people and interests as well as between interests and interests.
- Fully public: Your Tweets are public by default; anyone with an internet connection can view and interact instantly with your tweets.
- Rich hashtags: With the 140-characters limitation, every character counts, so users carefully select the hashtags they use in their tweets’ text.
Social network analysis (SNA) is an advanced form of analytics that is specifically focused on identifying and forecasting connections, relationships, and influence among individuals and groups. It mines transactions, interactions, and other behavioral information that may be sourced from social media, which may have been previously limited to CRM, billing, and other internal systems.
Why (online) Social Network Analysis
Social network analysis is a useful means of mapping the shape of virtual crowds. For example, visualizing social media conversations with a graph of relations between actors or between contents could help to answer some questions, such as:
- Who talks with whom?
- Who is the focal point of an online community?
- Which communities are involved in particular conversations?
- What are the more interesting topics?
Read full article on: Data Driven Journalism, WHERE JOURNALISM MEETS DATA