May 7-9, 2014 – SQLPASS – San Jose – Social media network maps in Excel with NodeXL

10MarMay 7, 2015 By Marc Smith

I will present a talk at the 2014 SQL PASS Business Analytics Conference in San Jose on May 8th.

The talk will focus on free and open tools for creating network maps and reports that can illuminate the landscape of social media.

The graph represents a network of 633 Twitter users whose tweets in the requested date range contained “sqlpass”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 15-day, 2-hour, 48-minute period from Tuesday, 25 February 2014 at 00:26 UTC to Wednesday, 12 March 2014 at 03:15 UTC.

There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and a self-loop edge for each tweet that is not a “replies-to” or “mentions”.

The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.

Top 10 Vertices, Ranked by Betweenness Centrality:
@sqlpass
@laertesqldba
@nikoneugebauer
@karlakay22
@jenstirrup
@retracement
@grrl_geek
@sqlrockstar
@markvsql
@sqlservermag

Top URLs in Tweet in Entire Graph:
http://www.sqlpass.org/ss2014launch/Webinars.aspx
http://www.sqlpass.org/summit/2014/Speakers/CallForSpeakers.aspx
http://sqlsaturday.com/287/schedule.aspx
http://bi.sqlpass.org/
https://attendee.gotowebinar.com/register/5759204879042322946
https://attendee.gotowebinar.com/register/2383471085904007682
http://paper.li/PASSAppDev/1391708680
https://attendee.gotowebinar.com/register/3165768110532010497
https://attendee.gotowebinar.com/register/2196441958679840002
http://www.linkedin.com/slink?code=bNfBsHy

Top Hashtags in Tweet in Entire Graph:
#sqlpass
#sqlserver
#sql
#sqlsatexeter
#sqlpass_de
#sqlsaturday
#sqlsatvienna
#sqlsatportugal
#msbi
#sqlsatmadison

October 26, 2010 – Social Tech 2010 – San Jose, CA

25OctMay 7, 2015 By Marc Smith

I spoke at the Social Tech 2010 conference in San Jose on October 26, 2010.

The event focused on social media technologies and practices in the business to business space.

I spoke on a panel about the application of social network analysis to social media to illustrate the ways SNA can illuminate the shape and key contributors to a social media crowd inside the firewall or on the public Internet.

Other notable speakers included: Thomas Lento from Facebook, Lawrence Liu from Cisco and Michael Wu from Lithium. The panel was moderated by Tatyana Kanzaveli.

An example of applying social network analysis and visualization is this map of the connections among the people who recently tweeted “socialtech” on the 25th of September, 2010.

The top most between users in this map can be sorted in the spreadsheet, bringing the user accounts for @mprofsevents, @socialtech, @mikewise07, @jowyang, @marketingprofs, @meganleap, @socialmediaclub, @cotweet, @zuberance, @spiral16.

It remains early in this cycle of the use of the socialtech tag, as the conference begins the volume will ramp up and new people will enter the graph bringing with them their unique patterns of connection and interaction. Over the coming days we may see more clusters emerge and new connections form.

As of Monday evening, the socialtech Twitter graph is small but densely connected with few isolates:

Vertices 87

Unique Edges 224

Edges With Duplicates 88

Total Edges 312

Graph Density 0.035017375

Single-Vertex Connected Components 6

By Tuesday morning, the event is gaining activity and density.

The most between members of this graph are the accounts:

@mprofsevents, @socialtech, @jowyang, @mikewise07, @cotweet, @marketingprofs, @socialmediaclub, marc_meyer, @meganleap, and anneyaz

The accounts @zuberance, @spiral16 have been pushed out of the top 10 most between positions in the graph, while @marc_meyer and @anneyaz join the graph.

Vertices 125

Unique Edges 470

Edges With Duplicates 112

Total Edges 582

Connected Components 10

Single-Vertex Connected Components 7

Maximum Vertices in a Connected Component 113

Maximum Edges in a Connected Component 575

Maximum Geodesic Distance (Diameter) 5

Average Geodesic Distance 2.725082

Graph Density 0.033419355

A paper about “EventGraphs” will be presented at the HICSS 2011 conference by Prof. Derek Hansen from the University of Maryland, School of Information.

April 19 -21, 2010 – Conference: eComm 2010: Emerging Communications – Video now available

01SepMay 7, 2015 By Marc Smith

I spoke at the eComm 2010 conference on April 20, 2010, talking about:

Mapping mobile social networks with NodeXL: finding key users, groups, and locations

The video is now available:

The video reviews the creation of maps like this:

That illustrate the connections among people who tweet the term “#ecomm2010”, scaled by the number of followers.

Abstract: Social network analysis (SNA) is a powerful method for gaining insight into the massive collections of connections created when many people connect to one another through mobile devices. SNA has been widely applied to desktop social media and is moving into the mobile world. Prominent studies of the “call graph” have been produced at national scales.

Mobile providers are applying SNA to identify key subscribers who can reduce churn and help gain adoption of new services and products. Network analysis has historically had a steep learning curve, but now new tools are making SNA easier for less technical users. This talk will describe social network concepts and their application to mobile data sets. A free and open add-in for the popular Excel 2007 spreadsheet called NodeXL (http://www.codeplex.com/nodexl) can perform many complex SNA tasks like data import, scrubbing, metrics calculation, clustering, and visualization. Applying this tool to call graph and subscriber data sets can reveal key positions in the network that can attract and hold other subscribers in the system.

Examples of network analysis of social media and mobile data sets can be found on the Connected Action blog (http://www.connectedaction.net).

ICWSM 2009 – Pictures and Posters

21MayMay 7, 2015 By Marc Smith

The recent 2009 ICWSM conference featured research into the nature of a wide range of social media.

[flickrset id=”72157618579371124″ thumbnail=”square” overlay=”true” size=”medium”]

Some highlights:
An Examination of Language Use in Online Dating Profiles
Meenakshi Nagarajan, Marti Hearst

Event Detection and Tracking in Social Streams
Hassan Sayyadi, Matthew Hurst, Alexey Maykov

Gephi: An Open Source Software for Exploring and Manipulating Networks
Mathieu Bastian, Sebastian Heymann, Mathieu Jacomy

Information Diffusion in Computer Science Citation Networks
Xiaolin Shi, Belle Tseng, Lada Adamic

Considering the Sources: Comparing Linking Patterns in Usenet and Blogs
Mary McGlohon, Matthew Hurst

PAPER: ICWSM 2009 – Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context

07MayMay 7, 2015 By Marc Smith

Our (Vladimir D. Barash, Marc Smith, Lise Getoor, Howard T. Welser ) poster paper, Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context has been accepted and published at the 2009 ICWSM (International Conference on Weblogs and Social Media) conference which will be held in San Jose, California this May 17, 2009 – May 20, 2009.

Abstract
Social media communities (e.g. Wikipedia, Flickr, Live Q&A) give rise to distinct types of content, foremost among which are relational content (discussion, chat) and factual content (answering questions, problem-solving). Both users and researchers are increasingly interested in developing strategies that can rapidly distinguish these types of content. While many text-based and structural strategies are possible, we extend two bodies of research that show how social context, and the social roles of answerers can predict content type. We test our framework on a dataset of manually labeled contributions to Microsoft’s Live Q&A and find that it reliably extracts factual and relational messages from the data.

Full Text: PDF: 2009 ICWSM Distingusihing knowledge versus social capital

Conference: 2009 International Conference on Weblogs and Social Media in San Jose

22MarMay 7, 2015 By Marc Smith

Another conference focused on research on blogs and other forms of social media is “ICWSM” – the International Conference on Weblogs and Social Media. I was able to attend the previous meeting of this conference last March in Seattle and give a talk about different classifications of social media and I am looking forward to attending this year’s meeting in San Jose. Last year we had a poster paper in the conference about the ways some users in a blog system called Wallop were able to hold other users in the system.

Some Users Pack a Wallop: Measuring the Impact of Core Users on the Participation of Others in Online Social Systems
Thomas M. Lento, Eric Gleave, Marc A. Smith, Howard T. Welser

There was also a paper about the lessons learned from managing large corporate online community efforts.

Space Planning for Online Community
Danyel Fisher, Tammara Combs Turner, Marc A. Smith

This year, we have a poster in the conference that is focused on the ways network structures created when people reply to one another can be used to predict whether a message or thread is a question and answer exchange or a long discussion or debate.

Distinguishing Knowledge vs. Social Capital in Social Media with Roles and Context
Vladimir Barash, Marc Smith, Lise Getoor, Howard Welser

The conference attracts some great people and features the state of the art in research at the intersections of computer science, natural language processing, social network analysis, search engine/information retrieval design, information visualization, knowledge management and the social sciences. That can be eclectic but this is the place for hearing about new work on Wikis, Blogs, Message Boards, and other social media systems like social networking services, micro-blogging systems, and mobile software.