People talk about the products and services the use, love or hate all the time in social media. These conversations can be better understood through perspective of social network analysis. Network theory views the world as a web of connected people. Network analysis builds measures and visualizations of collections of connections to reveal the key people, groups and issues in these conversations. Using social media network maps and reports the confusing landscape of tweets and posts comes into focus. Information visualizations of the virtual crowds of people gathered around every brand, product, event, or service highlights the range of variation in the shape of these crowds. Six different patterns have been identified so far, allowing social media managers to recognize the nature of the brand network they have and the nature of the network they want to have. Network measures are useful as KPIs for tracking not just the size and volume of a social media stream, but also the shape and structure of the pattern of connections. The six patterns: divided, unified, fragmented, clustered, and in and out hub and spoke, are a useful guide to strategic engagement in social media.
Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.
This Deep Dive will be an active event. We will mix thoughtful discussions with experiential activities, building social capital while we learn about social networks. Participants are encouraged to submit social media topics in advance so maps and reports can be generated for the event.
This April 8 and 9, 2013 an NSF funded workshop called Kredible.Net to be held at Purdue University will bring together researchers studying reputation and social roles in social media.
The grant will help researchers investigate how social media, especially Wikipedia articles and editors, shape public knowledge. The project aims to build a research community and to propose a research agenda for the study of reputation and authority in informal knowledge markets, such as Wikipedia.
“This collection of Science@Microsoft vignettes illustrates some of the progress that has been made in a number of disciplines and describes the technologies that have been deployed to gain these new insights.”
The volume lists tools for scientific research and includes NodeXL:
NodeXL is a powerful and easy-to-use interactive network visualization and analysis tool that uses Microsoft Excel for representing generic graph data, performing advanced network analysis, and visual exploration of networks. NodeXL supports multiple social network data providers that import graph data (nodes and edge lists) into Excel. The import features of NodeXL explore social media by pulling data from personal email indexes on the desktop, Twitter, Flicker, YouTube, Facebook, and web hyperlinks.
NodeXL allows non-programmers to generate useful network statistics and metrics quickly and create visualizations of network graphs. Filtering and display attributes can be used to highlight important structures in the network.