Coverage of this session by Jordan Ynostrosa of SocialMedia.org. Connect with her by following her on Twitter.

11:00 — SocialMedia.org’s Kurt Vanderah introduces Social Media Research Foundation’s Marc Smith.

11:01 — Marc’s main thesis: “Crowds Matter”. They happen not only in the physical world, but in the virtual world.

11:02 — Marc: What would it look like if we could actually see the crowd under a tweet stream? Crowds in social media have a hidden structure.

11:03 — Marc: Network analysis tells us interesting stories about the shapes of crowds. Our job is to build tools that will build an “x-ray” for social media — giving us insights into social media.

11:04 — Marc: Network diagrams help give us a feel about how different companies use social.

11:05 — Marc: NodeXLGraphGallery.org – you can see maps created in Xcel to look at the webs of connections.

11:06 — Marc: We envision hundreds of NodeXL data collectors around the world collectively generating an archive of social media network snapshots on a wide range of topics.

11:07 — Marc: Not all graphs are the same, but there are patterns that gather around your hashtags and your brands.

11:08 — Marc: We want to help you dive into the data – ex. Who are the 10 most central people in this discussion? Not all people who show up at the top of the list are the “A – Listers”.

11:09 — Marc: Some words matter to you more than others. These tools give insight into related hashtags, words…etc. (Does not always have to be twitter – it can be email, Flickr, Facebook…etc).

11:10 — Marc: We want to understand the different kinds of ties that link people together — Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in’s…

11:11 — Marc: We want you to be able to “Think Link”, with Nodes & Edges. Ex. A is related to B.

11:12– Marc: There are 6 kinds of social media networks: Divided (Polarized Crowds), Unified (Tight Crowd), Fragmented(Brand Clusters), Clustered(Community Clusters), In-Hub & Spoke(Broadcast Network), Out-Hub & Spoke(Support Network).

11:13 — Marc: 53% of iPhone Tweets have no @ sign in them.

11:14 — Marc: Social media network analysis – social media is inherently made of networks which are created when people link and reply. Collections of connections have an emergent shape, some shapes are better than others. Some people are located in strategic locations in these shapes and centrally located people are more influential than others.

11:15 — Marc: SNA questions for social media. What does my topic network look like? What does the topic I aspire to be look like? What is the difference between #1 and #2? How does my map change as I intervene? What does #YourHashtag look like? Who is the mayor of #YourHashtag?

11:16 — Marc: Find the people to follow on the upper end – on the lower end, find topics of interest that are relevant to them.

11:18 — Marc: Network phases of social media success. First, you get an audience. Then people mention you. Then the audience gets an audience. Finally, the audience becomes community.

Q&A:

Q: Smart tweet – if you had empirical evidence?

A: Marc: Some numbers that we had higher engagement because a tweet with a name, directed tweets get higher engagement. Tweets that go to high centrality users are ignored – they already have an audience and are busy. Engage with people that are interested.

A: Marc: @Theirname #TheirHastag news about your brand using their words http://your.site #YourHashtag – this way you getting to them with 2 channels – 2 ways to get their attention.

Q: One influencer that is relevant one moment can be completely irrelevant the next. How do you deal with this?

A: Marc: We use the last 7 or 8 days of data — look at it for a particular data set. Data from 6 months ago may not be relevant.

Q: What types of data do you need in Excel?

A: Marc: We can bring data in from a variety of sources. Twitter, flicker. wikis, email, Facebook. IT guys can also deliver data from other sources. Who shipped what to whom? Anytime you have a collection of links, you can now “Think Link”.

Q: When you define networks and mayors, how “Like” data appears? (on twitter)

A: Marc: “Like” data is hard to get out of twitter – we were looking at replies and mention. Any data that has a relationship between A & B you can find data from anywhere. The world is made up of real time data.

Q: How do you look at conversations on twitter vs. Facebook?

A: Marc: Twitter and Facebook are not the same. each of the platforms create distinct social structures. Facebook – little structures of networks. Twitter – the only place where people can have a path to province without a budget. How are all the platforms different – generating different structures crowds.