I participated in a webinar hosted by the Prospect Research Institute. We discussed the ways that NodeXL can simplify the task of collecting social media and social network data. The tool generates easy to understand reports that highlight insights into connected structures.
The slides associated with the talk can be found here:
This is a map of the network of 2,785 Twitter users whose recent tweets contained ““kansas state” OR KState” over the 1-day, 23-hour, 14-minute period from Monday, 13 January 2014 at 17:06 UTC to Wednesday, 15 January 2014 at 16:20 UTC.
Below is one of the few videos of the late sociologist Peter Kollock giving a lecture that I know of. It is a great example of Peter’s style: entertaining and rich with ideas and insights. The video captures Peter’s talk at Howard Rheingold’s Stanford class on “Literacy of Cooperation” – a review of computer-mediated collective action in Winter 2005. Peter speaks about strategies to avoid or resolve social dilemmas, covering the Tragedy of the Commons and Prisoner’s Dilemma situations, and applying these concepts to social media and Internet collective action.
A podcast from the Yi-Tan conference call was devoted to a discussion of Peter Kollock’s work.
If you know of additional videos of Peter, please send them my way! marc-at-connectedaction-dot-net
In this video interview, John and I summarize the themes discussed at the symposium including the political implications of inequality of technology access and the literacy to use it. John describes his efforts to map the global blogosphere and I describe the ways social media creates social networks that can be extracted and mapped. What does it take to be a communicator in a digital media environment? We discuss the privacy rights of public data and the use of data in ethical ways. Not everyone with a fiber-optic cable and server room operates under ethical guidelines. Given that digital communication is inherently traceable communication, could it be that not everyone should take the risks of communicating? Digital communication makes messages more findable and available which is a virtue when you want your message heard widely. It is getting harder to limit distribution of content to select audiences. I like to argue that the destiny of all information is to be made public if only because information never becomes less public.
IE University has a YouTube channel with lots of interesting video (in English and Spanish) related to communication, innovation, and social media.
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.
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.