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.
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.
The Israel Internet Association is the official Israeli Chapter of the Internet Society. Their annual meeting is a central event of academics (sociologists, psychologists, business and law) as well as industry participants from sectors including mobile cellular companies and internet service suppliers.
My talk title: Analyzing Internet social media: visualizing social networks in (mobile) computer networks
Abstract: Social media systems on the Internet are sociologically interesting: why do some online groups succeed where others fail? How do different collections of online media and populations of authors differ from one another? How do patterns of contribution vary and how do these differences illustrate the roles people play within their communities? Several visualizations of patterns of contribution and connection in a range ofInternet social media including web boards, enterprise social networks services, and personal email are presented to illustrate the range of variation among social media repositories and between types of contributors. These images suggest that a more comprehensive overview of social media can generate sociologically relevant findings, improve community management tasks as well as provide features that can improve search and ranking of user generated content. A freely available tool, NodeXL, will be demonstrated to perform basic social media analysis tasks. Extending these tools to include mobile social software (“mososo”) data sets is a major new direction. In the not too distant future, mobile devices will possess a range of sensors and become more “socially aware”. When phones routinely notice each other the nature of social interaction will change dramatically. How will places and locations change when machines become socially aware? In this talk, sociologist Marc Smith, Chief Social Scientist for Connected Action Consulting Group, a provider of social media analysis platforms and services, will describe these new technologies and some ways of thinking about their implications.
Video is now available from a panel hosted by the Women’s Affinity Group of O’Melveny & Myers’ Silicon Valley Office in Menlo Park on November 19th. Along with Karla Spormann, President and CEO Tendo Communications, Martin Eberhard, Co-founder and former CEO Tesla Motors, Patrick Ewers, Founder, Mindmavin LLC. We spoke about “Using Social Media to Grow and Market Your Business”.
We discussed ways to leverage social networks networks beyond personal connections – to provide business value. We talked about ways to efficiently and effectively use social media to market and grow your business.
Using social media to grow and market your business
I spoke about tools, like NodeXL, that we have been building that create maps of the relationships among a population of people gathered by some shared attribute, like mentioning a keyword or hashtag.
NodeXL network of related YouTube videos sharing the term "blend".
Starting in version 100 NodeXL has added a data import feature for extracting social networks from the associations between users and videos in YouTube. The new social media network data spigot offers insights into the ways YouTube is socially structured. This spigot joins the existing twitter, flickr, and email data import providers present in NodeXL. We plan to deliver a expanded flickr spigot soon and have been working with others to deliver a hyperlink and wiki network provider in the coming months. We have an interest in social media network data spigots for other networks data sources like SharePoint, Exchange, and LDAP servers.
There are two types of network that NodeXL can extract from YouTube: video and user networks.
Video networks are collections of related videos linked by a shared string in their title, keywords, description, categories, or author’s username.
The user network returns a set of YouTube user names and the links between them based on users friending and or subscribing to another user.
Each import dialog allows for a number of configurations over the volume and detail of the data set requested.
This is a good demonstration of several features in NodeXL: social media network importers (in this case from Twitter), the use of a variety of layouts, auto-fill column mappings, and dynamic filters to reveal some important structures, groups and people in the graph.
Note! Excel has issues with security and workbooks. The easiest way to use that file is to open a blank NodeXL template and use the “Import from older workbook ” feature to pull the data into a workbook that your copy of Excel *can* trust.
My colleague, Eduarda Mendes-Rodriguez, recently demoed NodeXL at the Microsoft EU Innovation Day event in Brussels, Belgium. She illustrates the value of network analysis in general with a social network diagram representing the voting patterns of United States Senators in 2007. The results show the clear party-line clustering and the presence of a few fence sitters, one of whomn just recently changed party. I think the difference between the two party’s internal cohesion is an interesting observation as well.
Eduarda does a great job demonstrating the ease with which a sophisticated analysis can be implemented via the NodeXL interface.
NodeXL is about to cross 8,000 downloads and has several releases in the works to add better tools for laying out a social network diagram.
On the theme of instrumenting the interaction order, here is Tanzeem Choudhury (from Dartmouth) giving a talk titled: “Using Sensors to Make Sense of People: Inferring the Micro and Macro Level Properties of Social Networks from Mobile Sensor Data” which she delivered March 2, 2009 as part of the Program on Networked Governance CCCSN Seminar.
This is a very informative lecture at Harvard from Tanzeem describing her Dartmouth group’s work to create, deploy and interpret the data from mobile inter-personal sensors.
