Feb 9, 2013

Arrows on Twitter

Twitter is a social network that network scientists refer to as an asymmetric network -- the links are directional, they are drawn with arrows.  Links between people on Twitter show direction of intent. The arrows are drawn from source to target.

Looking at a social graph from Twitter we can tell a lot by following the arrows...
  • who is aware of whom/what?
  • whom/what is getting attention?
  • who is involved in conversations on specific topics?
  • who is central, and who is peripheral to the discussions?
This past week I was invited to a Twitter Chat (a.k.a. Tweetchat) on the topic of Serendipity.  Two separate chat groups (#innochat and #ideachat) came together on a topic of overlapping interest.  Twitter chats last for 1 hour and use a pre-determined Twitter hash-tag to track all of the tweets in the on-line conversation. 

When we draw a network map (a.k.a. social graph) we see people as nodes and their connections/conversations as links.  In this case, the links have arrows showing who is referring to whom.

Let's first look at outgoing links -- who is linking out, to whom.  Links on Twitter can be of a broadcast nature (X announces something to all of her followers).  Links can also be directed at a specific target -- Y aims a message at a specific person, or Z re-tweets (RT) something X has posted.  Although an RT is a broadcast, it is also a message back to the originator of the tweet -- I am aware of what you tweeted, and I choose to pass it on to others (not necessarily an endorsement).  

The network map below shows participants from the Serendipity Tweetchat.  Two nodes are linked if the source node, RT'ed, MT'ed or @-messaged the target node more than once during the chat session.  The node colors show: blue - general chat participant, purple - chat facilitators, green - invited guest.  The Twitter ID of each participant is shown beneath their node.  The node size in this first map is determined by a network metric called Awareness -- it looks at all local, outgoing, direct and indirect, links surrounding a node.  The higher the awareness metric, the larger the node.  Larger nodes should be more aware of what is happening in the surrounding network, than the smaller nodes.

Next we look at the same map -- same nodes, same links -- but different node sizes.  This time the nodes are sized by incoming links.  The network metric used here is called Attention, it looks at all local, incoming, direct and indirect, links surrounding a node. It is good to have many incoming links, but it is even better to have incoming links from others who have many incoming links!  Those with a nice pattern of incoming links are what Malcolm Gladwell referred to as mavens in The Tipping Point.

Notice that some of the node sizes have changed drastically -- some with low Awareness, have high Attention and vice versa.  The metrics help reveal the roles people play on Twitter -- some engage many others, while some prefer/wait to be engaged (targeted).  The node of the lead chat organizer, @blogbrevity, is large for both network metrics -- a proper pattern for an effective facilitator!

Our third network graph shows node size based on both incoming and outgoing links.  The network metric, Integration, shows how "in the thick of things" a node is.  A Twitter node with a high Integration score is probably posting interesting tweets, noticing other peoples' tweets, getting retweeted  and participating in many conversations.

Notice how the both facilitators (purple nodes) have the largest nodes -- they were very active in moving this very rapid chat conversation forward.  They were interacting with the invited guest, with newcomers and regulars, all the while asking questions, and RT'ing key tweets of the ongoing conversation.  Those with a high integration metric can play the role of connectors, as described in Gladwell's Tipping Point.

Twitter is not just about person-to-person interactions, it is used to broadcast messages to large groups -- either followers or those tracking a hash-tag.  Many of the tweets in the chat, were aimed at no one in particular, they were broadcast to the whole group. This network map is different than the others, because it shows only broadcast messages to the whole group -- it does not show interactions between the participants.  The whole group of the chat participants are represented by the large red node in the center -- it is the hub.  The spokes around the hub represent various participants that shared more than one tweet with the whole gathering.  The thickness of the links indicate how many tweets each person/spoke sent to the group/hub.

This chat network formed, emerged, and disbanded all in the space of several hours.  Yet, it revealed the pattern of many long-term networks -- a core-periphery structure, mavens, connectors, and leaders.  Many of the participants in this chat, already knew each other on Twitter, especially through previous Twitter chat events.  This was really an old network reconvening -- with a few new members joining in.

The core members of a group are easy to spot, they are the ones with many arrows, all pointing to each other -- a sub-network where everyone seems to know, and interact with, everyone else.  The network map below shows the core of the network -- all nodes have at least 4 connections to everyone else and they all have incoming and outgoing arrows.  The core was so tightly packed that we removed the arrow heads so that the graph was easier to read.

Finally, we look at all of the data from this chat -- aggregate all of the arrows, and combine all of the maps above -- to find out which participants were most involved in this Twitter chat.  The list, sorted high to low, shows the fifteen (15) most connected people over the hour long chat on Serendipity.  


Next time you look at a map of a human network, look for the arrows.  Who are they going to?  Where are they coming from?  Where is a cluster of arrows, all pointing to each other?  Ask the analyst what the links mean?  What do the node colors/sizes mean?  Soon you will be able to make sense of the map and zero in on key clusters of activity, along with key connectors in getting things done.

To the connect the dots, follow the arrows!

Acknowledgements: One of the chat facilitators -- Andrew Marshall (@DrewCM) provided us the history of the chat from Tweetchat.com.  My friend, and colleague, Zee Spenser converted the PDF to CSV network mapping data.  Zee shares the data and his code on github.

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