Mapping Twitter Topic Networks:
From Polarized Crowds to Community Clusters
The paper documents the distinct patterns of connection that emerge when people talk to one another using social media services like Twitter. The paper includes six network visualizations that clearly demonstrate the diverse ways people connect to people when using online tools.
Ballroom ABNetworks are everywhere, particularly in social media. Understanding networks can quickly reveal the key people, groups, and topics that matter most. But the tools to collect, analyze, visualize, and gain insights into connected structures have remained complex. Now the free and open NodeXL application makes network analysis tasks as easy as making a pie chart. The Network Overview Discovery and Exploration add-in for Excel (2007, 2010, 2013) extends the familiar spreadsheet, enabling users to easily access networks from a range of data sources including Facebook, YouTube, Twitter, Flickr, email, message boards, Wikis, blogs, and other repositories of connections. With simple automation tools, NodeXL users can calculate a range of network metrics, create a visualization, and generate a report highlighting key people, groups, and top URLs, hashtags, words and word pairs used in the discussion network. Network maps have revealed many of the hidden structures of social media, defining the major differences in the shapes and structures created as people link to one another.
If you have questions on social network analysis, meet with Marc to talk about:
NodeXL and related network analysis and visualization tools
How to collect, store, analyze, visualize, summarize and publish social network reports with just a few clicks (and no coding)
How to identify key influential people and subgroups within a conversation network
How to apply social network analysis to social media marketing
How to apply organizational network analysis to enterprise collaboration
Above is a map of the connections among the people who recently tweeted the term “strataconf” over the 7-day, 19-hour, 38-minute period from Sunday, 26 January 2014 at 21:53 UTC to Monday, 03 February 2014 at 17:32 UTC. The key people in the network at this point are:
You can make these types of maps with just a few clicks using NodeXL.
Networks, no matter how complex, are composed of simpler, smaller structures, called motifs. Some of these structures are easy to identify, like the pattern of a “star” where a single node acts as the sole connection to a connected component for one or more “pendant” nodes with a single tie. Another common pattern are nodes that are “parallel bridges” which share the only two connections they have with two or more other nodes. These common structures can be identified and removed and replaced with more efficient and comprehensible representations.
The result is a simplification of the network visualization, removing clutter to reveal the core structural properties of interest.
A complex network of voting relationships in the
2007 United State Senate is reduced to a simplified form
This method for collapsing complex network graphs into simpler forms has been implemented in NodeXL. Look for the feature in the NodeXL Ribbon menu, in the NodeXL > Analysis > Groups > Group by Motif… option.
NodeXL implements network motif simplification
The feature allows users to select the types of motifs that should be recognized and collapsed:
[flickr id=”6234653454″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word #JW11 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
[flickr id=”6233561108″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word AOIR when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Connections among the Twitter users who recently tweeted the word AOIR OR #IR12 when queried on October 11, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
These are the connections among the Twitter users who recently tweeted the word OccupyWallStreet when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Graph Metric: Value
Graph Type: Directed
Unique Edges: 3014
Edges With Duplicates: 720
Total Edges: 3734
Connected Components: 318
Single-Vertex Connected Components: 300
Maximum Vertices in a Connected Component: 664
Maximum Edges in a Connected Component: 3326
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.508133
Graph Density: 0.002524525
The #Occupywallstreet movement is growing and lots of activity is taking place in social media. Here is a map of the connections among the people who recently tweeted the term “#occupywallstreet” on 8 October 2011.
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.
Social media and virtual worlds offer two important frontiers for measuring earned engagement. In both, audiences are actively engaged as participants. This workshop covered foundational concepts in media measurement, describe new frontiers in measuring audience engagement in social media and virtual worlds, and provided hands-on experience in using new analytical tools.
This session also provided a walk through the basic operation of NodeXL, including generation of social networks from social media data sources like personal e-mail (drawing data from the Windows Desktop Search engine) and the Twitter social network micro-blogging system. Arbitrary edge lists (anything that can be pasted into Excel) can be visualized and analyzed in NodeXL. Attendees were encouraged to bring an edge list of interest. Sample data sets were provided.