I visited the SONIC group at Northwestern University for a talk about social media networks on October 22, 2010.
Travel
Video: October 19, 2010: Marc Smith talk at the University of Michigan
I spoke at the University of Michigan, School of Information on October 19th, 2010 about “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures“.
I demonstrated the ways social network data sets can be extracted from social media services like Facebook, Twitter, email, YouTube, and flickr. These network graphs can reveal information about the “shape” of the population in terms of the presence of sub-groups and communities within the larger population. In addition, each individual participant is located or positioned within the graph, helping to identify the people who are “core” versus those who are peripheral, as well as those who occupy the position of “bridge” between two otherwise separate groups.
The Yahoo Speaker Series at the School of Information supports distinguished guest lecturers from the fields of information and technology.
2010 Speaker: Marc Smith
VIDEO: Marc Smith
Marc Smith, chief social scientist with the Connected Action Social Group, presented “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures,” on Tuesday, Oct. 19, 2010.
Smith’s talk was sponsored by the Yahoo! Speaker Series, Michigan Interactive & Social Computing, and the School of Information.
In this talk, Smith described how networks are a data structure common across all social media systems — systems defined by enabling populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. Using NodeXL, networks become as easy to create and analyze as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, Flickr, YouTube, and e-mail networks will be presented, illustrating different patterns created when communities, brands, and controversies are discussed.
How do airline twitter network maps compare? United v. Delta Airlines mapped in NodeXL
Last week I made a video with Norm Rose from Travel Tech Consulting about the ways different airlines get talked about in twitter. Norm explores new technologies that impact the travel business and he asked me to create two maps: one for United Airlines and the other for Delta Airlines. In the video below, we discuss these maps and what they mean for any kind of brand engaging in social media.
Travel Industry Social Media Analysis: United v Delta – How do airline twitter maps compare? from Marc Smith on Vimeo.
The two maps we discuss are: United Airlines:
and Delta Airlines:
United has a large twitter mentioning population and has a bigger main component. The larger profile photos indicate Twitter authors with many followers. The large population of isolates or small components at the bottom of both images are people who mention either airline but are not in a conversation or a relationship with someone else who also mentioned these brands. They are “shouts” about a brand, not conversations. In contrast the large component in both images are the connected collection of people who talk to people who talk about these brands. They are committed to the topic in a way the less connected authors are not. They know someone who also talks about air travel.
Viewed over time, we can start to assess the ways these brand’s twitter populations are changing. Are new people moving into central hub positions? Are people who held those positions drifting away?
A key observation is that some people with relatively few followers occupy highly central positions in the graph. This suggests that these authors have a location that lends their content attention from other well positioned people. Popularity is not just about volume of connections, in a social network perspective, importance is also a function of where the person sits within the graph.