I will speak this March 4th at the 2012 Predictive Analytics World in San Francisco about ” Crowd Photography for Social Media“.

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/speakers.php
http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#

Monday @ 5:25-5:45pm

Track 1:
Social Data Case Study:
Social Media Research Foundation
Crowd Photography for Social Media

Crowds of people gather in social media around many products, services, businesses, and events but they can be difficult to see and understand. With new free and open tools, it is now possible to map and measure social media spaces, capturing the sub-groups and key people within and between them. Learn how to capture social media data and quickly generate a visual map of the crowd. With maps in hand, we will discuss ways they guide a journey to the key influencers and concepts in the crowd.

Speaker: Marc Smith, Director, Social Media Research Foundation

Mapping social media networks (with no coding) using NodeXL

Time: 16:50 on 01 Mar 2012.

Session type: 40 minute presentation

Topics: Visualization & Interface

Description: Maps of the complex connections that form when people link, like, reply, rate, review, favorite, friend, follow, edit, and mention one another can reveal important trends. It is possible to create network maps with free and open tools that identify key people and sub-groups in any social media population with just a few key clicks. Can you make a pie chart? You can now make a network chart.

Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation’s (http://www.smrfoundation.org) free and open NodeXL project (http://nodexl.codeplex.com) makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.

We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.

I will present a talk at Personal Digital Archiving 2012 titled “Arc-chiving: saving social links for study“.

The conference will be held on Thursday-Friday, February 23-24, 2012 at the Internet Archive in San Francisco.

News and updates on the conference will be posted at the conference web site, http://personalarchiving.com.

My talk this year will focus on collecting and analyzing connections between digital objects (like users) and the insights these tools make possible.

Abstract: While digital content is archived in various ways, the “arcs” or links among people and their digital objects are not systematically saved. Efforts to store social media often overlooks including data about collections of connections. The Social Media Research Foundation is dedicated to open tools, open data, and open scholarship related to social media. It is producing tools that can collect, analyze and upload social media data, including the arcs that link people and objects. Using the free and open NodeXL application, users can collect, analyze and visualize complex networks and then upload the data to a growing archive on the web at NodeXLGraphGallery.org. As the group of researchers grows, an archive is being assembled to provide researchers around the world with the data about social media needed to understand the ways computer mediated communication tools shape society.

My talk at the 2011 Personal Digital Archiving conference is available through the Internet Archive’s video service:

Mastering Social Media 2011 is a workshop scheduled for November 28 and 30 in Cape Town and Johannesburg, South Africa.

My partner, Walter Pike, is a Marketing Maven and Founder of PiKE | New Marketing (www.pike.co.za) and the founder of the Digital Academy www.digitalacademy.co.za
He blogs at walterpike.com.

Mastering Social Media will give you practical tools on how to plan, execute and monitor your social media campaigns. Discussions will lead you through the introduction to social media marketing, understanding community dynamics, mapping social networks and applying network insights to your goals.
Brand Managers, Marketing Managers, Advertising Agencies, Digital Agencies, and PR Agencies are likely to find the day useful.

Venues and Dates

Johannesburg

30 November 2011
Gordon Institute
of Business Science,
Illovo

Pictures:

Methodologies for Web and Social Media Data Analysis in Social Science and Policy Research

CCSR Short Course
Social Media Network Analysis using NodeXL

November 9th  9.00 am – 5.30 pm.

Marc Smith
Social Media Research Foundation
http://www.smrfoundation.org

Course Summary: Networks are everywhere in the natural and social world.  New tools are making the task of getting, processing, measuring, visualizing and gaining insights from network data sets easier than ever before.  The rise of social media offers a new and abundant source of network data.  The NodeXL project (http://www.codeplex.com/nodexl) from the Social Media Research Foundation (http://www.smrfoundation.org) offers a free and open path to network overview, discovery and exploration within the context of the familiar Excel spreadsheet.  In this short course we will introduce the NodeXL application and review the landscape of networks, social networks, and social media networks. Using the tool, non-programmers can quickly select a network of interest from various social media and other data sources.  Twitter, flickr, YouTube, email, the World Wide Web, and Facebook data can be quickly imported into NodeXL.  Networks can then be analyzed and visualized using tools similar to those used to create a pie chart or line graph [1].  As the challenge and cost of network acquisition and analysis drops, abundant data sets are being generated that document the range of variation of diverse sources of social media.  How many different kinds of Twitter hashtags exist?  Using snapshots of hundreds of hashtags collected over a year, it is now possible to build rough taxonomies of this kind of social media.  NodeXL provides access to a web gallery of data [2], allowing users to browse existing data sets and upload their own as well. Borrowing the vision of telescope arrays that create composite images far better than any individual instrument could, the Social Media Research Foundation envisions an user generated archive that provides a research asset that supports the collective effort to understand the structures and dynamics of network data.

[1] NodeXL Image Gallery: http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
[2] NodeXL Graph Gallery: http://nodexlgraphgallery.org

Course Objectives
After this course, participants will:

(1) Be familiar with the basic concepts of networks, social networks and social media networks
(2) Understand the core features of the NodeXL network analysis and visualization tool
(3) Review images and data sets for dozens of different social media networks
(4) Learn to identify general types of social media networks along with the key people and groups within them

Target Audience
This course is suitable for people with some experience or interest in social media, social science, or social network analysis.  It is particularly appropriate for those who are involved in studying social structures and their change over time.

Laboratory and IT requirements:
Participants will need access to a computer connected to the Internet  and will be supplied with the free NodeXL software.

Suggested Reading
Analyzing social media networks with NodeXL: Insights from a connected world
http://www.amazon.com/gp/product/0123822297?ie=UTF8&tag=conneactio-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0123822297

EventGraphs:
http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13

Visualizing the Signatures of Social Roles in Online Discussion Groups:
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Discussion catalysts in online political discussions: Content importers and conversation starters
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-JCMC-Discussion-Catalysts-Himelboim-and-Smith.pdf

Analyzing (Social Media) Networks with NodeXL
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-CT-NodeXL-and-Social-Queries-a-social-media-network-analysis-toolkit.pdf

Whiter the experts: Social affordances and the cultivation of experts in community Q&A systems
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-Social-Computing-Whither-the-Experts.pdf

First steps to NetViz Nirvana: evaluating social network analysis with NodeXL
http://www.cs.umd.edu/~cdunne/pubs/Bonsignore09Firststepsto.pdf

]]> http://www.connectedaction.net/2011/11/05/november-8-2011-university-of-manchester-nodexl-sna-social-media-workshop/feed/ 0 http://www.connectedaction.net/2011/10/28/event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011/?utm_source=rss&utm_medium=rss&utm_campaign=event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011 http://www.connectedaction.net/2011/10/28/event-localsocialsummit11-and-social-media-research-foundation-reception-november-10th-2011/#comments Fri, 28 Oct 2011 08:00:30 +0000 Marc Smith http://www.connectedaction.net/?p=5260 & 

On November 9th and 10th, 2011 the LocalSocialSummit11 will be held in London.

On Thursday morning, November 10th, Doctor Bernie Hogan from the Oxford Internet Institute and member of the Social Media Research Foundation will speak on the topic:

Insights: Social Network Analysis On Facebook Data, with a local twist

Thursday at 3:45 I will speak at the event on:

Charting Collections Of Connections In Social Media

Following the conference from 5 to 6pm will be a reception for the Social Media Research Foundation.

Please join us for refreshments and a discussion of the ways social media analysis tools can guide your engagement with stakeholders around the world.

The event is held at wallacespace:

22 Duke’s Road, London, WC1H 9PN
t +44(0)20 7395 1265
f +44 (0)20 7836 9591
e ask@wallacespace.com

22 Duke’s Road is only minutes walk away from Euston, St Pancras International, King’s Cross and Russell Square Tube.

Registration form for attending the event is below….

I presented on social media network analysis on October 20, 2011 in New York City at Predictive Analytics World.

A map of the connections among the people tweeting about the #Pawcon hashtag is below.

Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding

Abstract: Networks are everywhere except the end user desktop.  NodeXL, the free and open network overview, discovery and exploration add-in for the popular and familiar Excel (2007/2010) spreadsheet allows users who are comfortable making pie charts to now make useful network visualizations.  Developed and released by the Social Media Research Foundation, NodeXL uses Excel as a framework, providing a GUI network browser (a “web browser”?) that novices can use quickly and experts can use to generate sophisticated results.  Data importers provide access to a range of social media network data sources like Twitter, flickr, YouTube, Facebook, email, the WWW, and more through standard file formats (CSV, GraphML, Matrix).  Simple to use tools can automatically analyze, visualize and highlight insights in complex network graphs.  Using NodeXL, researchers have been collecting a wide range of network data sets from various social media services.  These images reveal a range of common social formations in social media and point to people who occupy strategic locations in these graphs.

This is a map of the connections among the people who tweeted the term “PAWCON” on the first day of the event:

These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/ph…

Top most between users:
@tapan_patel
@pawcon
@sasanalytics
@deloitteba
@kristinevick
@jamet123
@zementis
@kdnuggets
@tibcospotfire
@saspublishing

Graph Metric: Value
Graph Type: Directed
Vertices: 41
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Self-Loops: 44
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 1.0.1.179

Here is an example map of the connections among the people who tweeted the term “pawcon” in Twitter on September 14th, a week prior to the event.

Manu Sharma, Principle Research Scientist at LinkedIn gave a great presentation on the patterns found in their data.  Big data, for example, showed that most of the people who previously worked at recently failed banks and financial institutions have updated their profiles to show that they mostly have new jobs at some of the remaining companies in the industry.

The event was held at the New York Hilton: Maps & Directions

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.

See: www.jivesoftware.com/jiveworld

A larger version of the image is here: www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph…

Top most between users:
@jivesoftware
@gialyons
@mikefraietta
@cflanagan
@cosmopolitan_lv
@ginorossi
@kristinhersant
@thebrandbuilder
@alanlepo
@mor_trisha

Graph Metric: Value
Graph Type: Directed
Vertices: 345
Unique Edges: 3606
Edges With Duplicates: 2072
Total Edges: 5678
Self-Loops: 632
Connected Components: 11
Single-Vertex Connected Components: 9
Maximum Vertices in a Connected Component: 334
Maximum Edges in a Connected Component: 5659
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.353001
Graph Density: 0.034184361
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

http://ir12.aoir.org/

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.

See: ir12.aoir.org/

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

Graph Metric: Value
Graph Type: Directed
Vertices: 98
Unique Edges: 119
Edges With Duplicates: 672
Total Edges: 791
Self-Loops: 153
Connected Components: 40
Single-Vertex Connected Components: 35
Maximum Vertices in a Connected Component: 55
Maximum Edges in a Connected Component: 710
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.257477
Graph Density: 0.035766884
NodeXL Version: 1.0.1.179

By expanding the query to include #IR12, the conference hashtag, the network expands to include:

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.

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

Graph Metric: Value
Graph Type: Directed
Vertices: 231
Unique Edges: 1984
Edges With Duplicates: 2118
Total Edges: 4102
Self-Loops: 586
Connected Components: 46
Single-Vertex Connected Components: 40
Maximum Vertices in a Connected Component: 181
Maximum Edges in a Connected Component: 4012
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.213887
Graph Density: 0.046320346
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

This year the IEEE Social Computing conference is being held in Boston, October 9-11, 2011.

The NodeXL team from the Social Media Research Foundation have a paper on our newest layout feature in NodeXL: Group-in-a-box.

Abstract: Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.

The paper is authored by:

Eduarda Mendes Rodrigues*, Natasa Milic-Frayling†, Marc Smith‡, Ben Shneiderman§, Derek Hansen¶
* Dept. of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal – eduardamr @ acm.org
† Microsoft Research, Cambridge, UK -natasamf @ microsoft.com
‡ Connected Action Consulting Group, Belmont, California, USA – marc @ connectedaction.net
§ Dept. of Computer Science & Human-Computer Interaction Lab, University of Maryland, College Park, Maryland, USA – ben @ cs.umd.edu
¶ College of Information Studies, University of Maryland, College Park, Maryland – dlhansen @ umd.edu

A map of the connections among the people who recently tweeted #SocialCom2011:

Connections among the Twitter users who recently tweeted the word #socialcom2011 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6232130442/sizes/l/in/ph…

Top most between users:
@danielequercia
@gadgetman4u
@bkeegan
@shaunlawson
@maryheston
@mmiiina
@ronaldomenezes
@theshadowhost
@fergal_reid
@cosleydr

Graph Metric: Value
Graph Type: Directed
Vertices: 36
Unique Edges: 119
Edges With Duplicates: 155
Total Edges: 274
Self-Loops: 105
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 35
Maximum Edges in a Connected Component: 273
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.174551
Graph Density: 0.107936508
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

The Future of Technology in Education workshop met on 7 October 2011 in London at the University of London Computer Centre (ULCC) to discuss the ways technology is changing the learning experience.

The hashtag for the event is #FOTE11 so I made a NodeXL SNA Map -

The following is a map of the connections among the Twitter users who recently tweeted the word #fote11 when queried on October 7, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: fote-conference.com/wordpress/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6221247656/sizes/l/in/ph…

Top most between users:
@fotie
@jamesclay
@timbuckteeth
@hopkinsdavid
@dajbelshaw
@eingang
@fredgarnett
@pressfuturist
@grahambm
@eyebeams

Graph Metric: Value
Graph Type: Directed
Vertices: 251
Unique Edges: 2904
Edges With Duplicates: 1908
Total Edges: 4812
Self-Loops: 730
Connected Components: 7
Single-Vertex Connected Components: 6
Maximum Vertices in a Connected Component: 245
Maximum Edges in a Connected Component: 4803
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.246839
Graph Density: 0.052988048
NodeXL Version: 1.0.1.179

The data set for this network is available from the NodeXLGraphGallery.

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Connections among the Twitter users who recently tweeted the word Oracle when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@benioff
@oracleopenworld
@salesforce
@arstechnica
@sai
@oracledatabase
@sapjobswithace
@kirbylunger
@capgemini
@reuters_biz

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2747
Edges With Duplicates: 827
Total Edges: 3574
Self-Loops: 1102
Connected Components: 425
Single-Vertex Connected Components: 396
Maximum Vertices in a Connected Component: 541
Maximum Edges in a Connected Component: 2923
Maximum Geodesic Distance (Diameter): 10
Average Geodesic Distance: 3.475208
Graph Density: 0.00226026
NodeXL Version: 1.0.1.179

A related map represents the connections among people who tweeted the term #OOW11

These are the connections among the Twitter users who recently tweeted the word oow11 when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.oracle.com/openworld/live/on-demand/index.html

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@oracleopenworld
@oracle
@oracletechnet
@rwang0
@devops_borat
@salesforce
@mysql
@emcoracle
@oraclemix
@oracledatabase

Graph Metric: Value
Graph Type: Directed
Vertices: 670
Unique Edges: 5882
Edges With Duplicates: 1573
Total Edges: 7455
Self-Loops: 881
Connected Components: 45
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 626
Maximum Edges in a Connected Component: 7405
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.770009
Graph Density: 0.013401602
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The Workshop on Information in Networks 2011 was held at NYU September 30-October 1st.

Over the course of the event a number of people tweeted using the hashtag #win2011.

These are the connections among the Twitter users who recently tweeted the word win2011 when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: winworkshop.net/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here:http://www.flickr.com/photos/marc_smith/6209064926/sizes/l/in/photostream/

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:

@sinanaral
@winworkshop
@barrywellman
@ladamic
@seanjtaylor
@drewconway
@chrisdiehl
@dylanwalker
@marc_smith
@ariegoldshlager

Graph Metric: Value
Graph Type: Directed
Vertices: 38
Unique Edges: 227
Edges With Duplicates: 193
Total Edges: 420
Self-Loops: 68
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 38
Maximum Edges in a Connected Component: 420
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.836565
Graph Density: 0.194167852
NodeXL Version: 1.0.1.179

These are the connections among the Twitter users who tweeted the word win2011 when queried a few days earlier on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6198785387/sizes/l/in/ph…

Top most between users:
@sinanaral
@winworkshop
@ariegoldshlager
@drewconway
@chrisdiehl
@kateaanderson
@dylanwalker
@barrywellman
@kristinalerman
@pboothe

Graph Metric: Value
Graph Type: Directed
Vertices: 16
Unique Edges: 60
Edges With Duplicates: 45
Total Edges: 105
Self-Loops: 26
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 16
Maximum Edges in a Connected Component: 105
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.617188
Graph Density: 0.270833333
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

At the end of the conference on October 5th, the network of connections among the people who tweeted about Wikisym looked like this:

These are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections are created when users reply, mention or follow one another.

See: www.wikisym.org/

Top most between users:
@edchi
@jfelipe
@phauly
@readermeter
@hfordsa
@wikisym
@clifflampe
@staeiou
@geoplace
@wikimedia

Graph Metric: Value
Graph Type: Directed
Vertices: 170
Unique Edges: 1163
Edges With Duplicates: 2165
Total Edges: 3328
Self-Loops: 658
Connected Components: 5
Single-Vertex Connected Components: 3
Maximum Vertices in a Connected Component: 165
Maximum Edges in a Connected Component: 3319
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.385502
Graph Density: 0.052941176
NodeXL Version: 1.0.1.179

In contrast, a few days earlier, these are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@wikisym
@jfelipe
@andicat
@geoplace
@staeiou
@dirkriehle
@wikimedia
@edchi
@bkeegan
@hfordsa

Graph Metric: Value
Graph Type: Directed
Vertices: 78
Unique Edges: 459
Edges With Duplicates: 354
Total Edges: 813
Self-Loops: 132
Connected Components: 10
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 68
Maximum Edges in a Connected Component: 800
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.207506
Graph Density: 0.086913087
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Marc Smith on Twitter.

I have enjoyed and been educated by my previous visits to South Africa and the Mobile Web in Africa conference, now in its 3rd year.

MWA gathers the many parts of the picture of mobile technologies in play across the African continent.

I will speak again at this year’s event, focusing on social media networks.  My partner is Walter Pike, a marketing professional with a specialty in digital media.

Here is the agenda for the workshop on Friday, November 25th.

Photos from the prior two years of Mobile Web Africa after the jump:

2010

2009

The recent Strata Conference in New York City gathered many leaders in big data, data mining, and information visualization.

Link: www.flickr.com/photos/marc_smith/6203313170

Connections among the Twitter users who recently tweeted the word Strata when queried on September 20, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: strataconf.com/summit2011

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6203313170/sizes/l/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@timoreilly
@strataconf
@doctorow
@acroll
@ticbeat
@datacenter
@hnshah
@oreillymedia
@markmadsen
@unglobalpulse

Graph Metric: Value
Graph Type: Directed
Vertices: 1118
Unique Edges: 5813
Edges With Duplicates: 6522
Total Edges: 12335
Self-Loops: 1010
Connected Components: 9
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 1110
Maximum Edges in a Connected Component: 12327
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.575622
Graph Density: 0.00647018
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Summer Social Webshop
on
Technology-Mediated Social Participation
University of Maryland, College Park
August 23-26, 2011

Eventful.  The 2011 Webshop at the University of Maryland was certainly that with both an earthquake and a hurricane to mark the start and end of the event.  We really moved heaven and earth at this workshop.

In 4  days, 20 talks, 45 students, an earthquake, a hurricane and many new connections – the Webshop touched on a set of related concepts, methods, and findings about ways to use communication and computation technology to help groups, neighborhoods, cities, states, and nations work collectively towards common goals.

Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers.  I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically.   The students who attended include some of the now leading researchers in the field of social science studies of the internet.  There is an impressive alumni list.

The last Webshop was held in 2003 and many years and significant changes have occurred in the time since. Twitter, Facebook, StreetView, iPad,FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run.  Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind.  A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.

After many years of inactivity, the Summer Social Webshop (@Webshop2011) happened again!  With the generous support of the National Science Foundation and additional assistance from Google Research, on August 23-26, 2011 at the University of Maryland, College Park, a group of students heard and engaged with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives.  Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event gathered students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies.   Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines applied to attend the 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explored the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.  The workshop attracted graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.

-

Organizers

Alan Neustadtl (@smilex3md) - Sociology, University of Maryland
Jennifer Preece (@jenpre) – iSchool, University of Maryland
Marc Smith(@Marc_Smith) - Social Media Research Foundation
Ben Shneiderman (@benbendc) - Computer Science, University of Maryland
PJ Rey (@pjrey) - Sociology, University of Maryland, Student Coordinator

-

Photos

2011 ASA meetings are being held in Las Vega, Nevada.  Several sessions are related to the study of the social uses and effects of information technology are hosted by the Communications and Information Technology Section of the American Sociological Association (CITASA).

These are the connections among the people who recently tweeted the term “ASA2011″ on 18 August 2011.

Several papers and panels related to the sociology of the internet will take place:

Saturday, Aug 20 – 2:30pm – 4:10pm

 124. Section on Sociology of Law Paper Session. Privacy in the Digital Age: Law, Culture, and Contention I (co-sponsored with the Section on Collective Behavior and Social Movements and Section on Communication and Information Technology)

Monday, Aug 22 – 8:30-9:30AM Roundtables

338. Section on Communication and Information Technology Roundtable Session

Monday, Aug 22 – 9:30-10:10AM Business and Awards Ceremony

(immediately follows roundtables)

Monday, Aug 22 – 10:30AM – 12:10PM 

376. Section on Communication and Information Technology Invited Session. Social Media in Community Action and Social Change

Monday, Aug 22 – 2:30pm – 4:10pm

419. Section on Communication and Information Technology Paper Session. New Media Frontiers: Youth and New Media

Monday, Aug 22 – 5:00-7:00PM, Section Reception, hotel suite, Caesars Palace

Location to be announced at all CITASA sessions and meetings.

Tuesday has the main day of panels and talks:

Tueday, August 23 – 10:30am – 12:10pm

514. Section on Sociology of Culture Invited Session. Privacy in the Digital Age II: Law, Culture, and Contention (co-sponsored with the Section on Collective Behavior and Social Movements and Section on Communication and Information Technology)

Regular Session. Social Participant On and Off the Internet.

Session Organizer: Barry Wellman (University of Toronto)
Sunday 1030-12

The Active Few and the Sporadic Many: Making Sense of Opinion Dynamics in Online Review Systems
*Fedor Dokshin (Cornell University)

The Internet, Young Adults and Political Engagement around the 2008 Presidential Elections

*Aaron Shaw (University of California-Berkeley), Eszter Hargittai (Northwestern University)

Social movements’ use of information and communication technologies: diffusion, usefulness and empowerment

*Piotr Konieczny (University of Pittsburgh)

*=presenter

———————————–

Regular Session. Networked Individuals On and Off the Internet

Scheduled Time: Sun, Aug 21 – 2:30pm – 4:10pm

Session Participants:

Session Organizer: Barry Wellman (University of Toronto)
Presider: Barry Wellman (University of Toronto)

“If you’re not texting me, you must be old:” Age Cohort Attachment to Mobile Phones
*Amanda M. Warr (University of Alabama at Birmingham), Shelia R. Cotten (University of Alabama at Birmingham), William A. Anderson (University of Alabama at Birmingham)

The Intersection of Gender and Place in Online Health Activities
*Melinda Goldner (Union College), Timothy M. Hale (University of Alabama at Birmingham), Shelia R. Cotten (University of Alabama at Birmingham), Michael J. Stern (College of Charleston), Patricia Drentea (University of Alabama-Birmingham)

Core Discussion Networks Over Time: How the Internet Reduces Social Isolation and Increases Network Diversity
*Keith N. Hampton (University of Pennsylvania), Lauren Sessions Goulet (University of Pennsylvania)

Social Network Sites, Social Ties, and Social Capital
*Randy Lynn (George Mason University), *James C. Witte (George Mason University)

Discussant: Barry Wellman (University of Toronto)

———————————————————-

Regular Session. Digital Divides in the Internet Age
Scheduled Time: Mon, Aug 22 – 4:30pm – 6:10pm

Session Organizer: Barry Wellman (University of Toronto)
Presider: Barry Wellman (University of Toronto)
Cyberasociality and the Online Sociality Divide: Third Level Digital Divide?

*Zeynep Tufekci (University of Maryland-Baltimore County)
(Tech) Culture in Action: Using a cultural framework to describe and explain differences in technological competency

*Cassidy Puckett (Northwestern University)
Does the “Do-it-yourself approach” reduce digital inequality? Lack of digital skills and infrequent Internet use

*Uwe Matzat (Eindhoven University of Technology), Bert M. Sadowski (Eindhoven University of Technology)
Stalled Digital Divide: Gender Differences in the Use of ICT in Taiwan

*Yu-Hua Chen (National Taiwan University), Wen-Yin Chien (United Daily News)

 

Please vote on our SxSW 2012 panel:

Thanks for your vote for our SxSW panel!

Here is a map of the connections among the people who tweeted the term “AEJMC” on August 7, 2011:

The top most between people in this network are:@aejmc, @jlab, @karenrussell, @terryflynn, @natcomm, @tmccorkindale, @derigansilver, @tkell, @aejmconlineads, and @jeremyhl:

I will present a Workshop on Social Media Network Analysis and NodeXL at the 9 August 2011 – Association for Education in Journalism and Mass Communication (http://www.aejmc.com) in St. Louis, Missouri along with my colleague Professor Hernando Rojas, from the School of Journalism & Mass Communication, University of Wisconsin – Madison.

See: http://www.aejmc.com/home/events/annual-convention/

The Association for Education in Journalism and Mass Communication (AEJMC) is a nonprofit, educational association of journalism and mass communication educators, students and media professionals. The Association’s mission is to promote the highest possible standards for journalism and mass communication education, to cultivate the widest possible range of communication research, to encourage the implementation of a multi-cultural society in the classroom and curriculum, and to defend and maintain freedom of communication in an effort to achieve better professional practice and a better informed public.

Our session is:

Using NodeXL for Social Network Analysis
Tuesday — 2 pm to 5 pm
Presented by Communication Theory and Methodology Division
This pre-conference workshop examines social network analysis. Social network analysis can be used to examine message boards, blogs, and friend networks (amongmany other phenomena). Participants will learn to use the NodeXL program to conduct a network analysis. For information, contact Michel M. Haigh, Pennsylvania State University at mmh25@psu.edu.

On June 22nd in New York City Wired Magazine and the Economist hosted a day of discussions and displays related to the theme of “Nextwork” hosted by Juniper Networks and located at the Tribeca Rooftop.

The Social Media Research Foundation displayed and printed  Nextwork Networks maps of the connections among the people who tweeted about the conference, the speakers, and their companies.

Visit the NextWork Network booth hosted by the Social Media Research Foundation to see a map of the connections among people who tweeted the term “NextWork“.  Networks are important data structures but the tools to access, analyze and visualize networks have been hard to use.  New tools now make networks as easy to handle as making a pie chart.  NodeXL, the free and open network overview, discovery and exploration add-in for the familiar Excel spreadsheet, makes creating network diagrams and gaining insights much simpler (see: http://nodexl.codeplex.com).  Using NodeXL, we have been collecting all the tweets that mention the NextWork event and building a network map of the ways those people connect to one another.  Not everyone is equally connected: some people occupy more strategic locations in the web of connections than others.   Find out who: request your own NodeXL social media network map for your Twitter account or the name of your company or brand (or your competitors) by dropping by the booth (or visit: http://www.smrfoundation.org/nextwork2011).

Here is an example, a map of the connections among the people who recently tweeted the name “Kevin Kelly”, one of the speakers at the event:

This is a map of the connections among the people who tweeted the term “Nextwork”:

The 5th Int’l AAAI Conference on Weblogs and Social Media
19-21 July 2011, Barcelona, Spain
Sponsored by the Association for the Advancement of Artificial Intelligence.

The fifth International Conference on Weblogs and Social Media brings together researchers from the disciplines of NLP, Social Psychology, Data Mining, Sociology and Visualization to increase our understanding of social media in all its incarnations. Research that blends social science and technology is especially encouraged.

Vladimir Barash is attending the 2011 ICWSM and reports from the sessions.

These are rough notes from Day One of ICWSM.

Manuel Castells – Keynote. Social Media and Wiki-Revolutions
–social media and social change
–what makes human human = meaningful communication
—changes in the nature of communication process => changes everywhere in society
—-more specifically, changes in the nature of communication process => changes in power relationships

—(mass-self communication) = social media
—-organized by large corporations but driven by desires of communicators (people)
—-this principle is embedded in the technical structure of the internet
—–enterpreneurial character, hacker culture

–culture of autonomy
–individuation != individualism
—referential point for society in what groups of individuals decide is important for them (as opposed to cultural / societal institutions)

–Castells study of internet usage in Catalonia
—empirical test of Internet as a platform of autonomy
—results: top 20% of population ranking high on autonomy according to several personality-based characteristics use the internet the most
—suggest there is a synergistic interaction between autonomy and use of the Internet(?)

–power and counter-power = dynamics of society

–growth of social media and increasing impact on social change over last ten years:
—Korea, Ukraine, Spain, US (Obama campaign)
—key is interaction of local / grassroots and the internet
—Arab spring
—-Note the incredible power of social movement e.g. Syria where people continue to demonstrate despite (because of?) hundreds of deaths

–Economic conditions NOT a sufficient cause of social change
—the sufficient cause = feeling of powerlessness to change things (including economic conditions)
—this powerlessness is transformed into empowerement via a feeling of togetherness
—always linked to a key event that brings the people together (e.g. Al-Wazizi self-immolation)
—this event needs to be communicated
—in the case of arab spring, communication = social media + mass media (Al-Jazeera)
—-Al-Jazeera worked to integrate news with mobile phones, mass media with social media
—once the event is communicated, the population needs to be organized
—-organizing force = young people. Both because younger more often agents of social change and because this generation grew up with the transformation of communcation
—once organization emerges in social media, it then moves “to the streets” and reaches individuals who don’t have regular access to social media

–Example: Egypt
—key event = Tunisian demonstration.
—First slogan = “Tunisia is the solution” (vs. “Islam is the solution”)
—But also events before-hand, such as 2008 massacre at factory where an anti-government protest took place
—this is key: revolutions do not happen merely because a younger generation wills them, they come out of exisitng sociopolitical pressures
—-broader point: the critical thing is not the technology itself, but the kind of message the technology enables to communicate conditions of struggle, humiliation, etc.
—-As a particular example, the Arab spring would not have happened without connections in social media that then moved to urban networks. Previously, revolutionary movements in the area tried strikes, etc. and were unsuccessful
—-these revolutions were not exclusively caused by social media – they were caused by injustice and humiliation. But they were *enabled* by social media

—Egypt also an example of attempt by government to shut off the internet, ultimately unsuccessful.
—-Huge economic losses
—-Couldn’t shut off everything: can’t control small-scale ISPs
—-Workarounds: speak2tweet, distribution of information via phone and fax networks (cooperation of hacker communities, Google, etc.)
—-Most importantly, the movement was already underway
—–A government that wants to control social movements that are enabled via social media needs to cut off social media, on a regular basis

—People say that Arab spring unique because these societies are highly repressive, but over 60% people worldwide feel un-represented by their government

–Example: Indigation movm’t in Spain
—started with a manifesto saying that the government is essentially out of touch with the people, and the economic situation is highly unfair
—manifesto led to peaceful demonstrations a week before election
—then self-organized debates and discussions in the cities
—integration of the internet and offline communication
—keys: 1. Anti-organization, anti-leadership. 2. Anti-violence. 3. No deliberate short-term goal. “We are slow, because we go far.” (vs. constitution, etc.) Long time to create true democracy.

Session: Location, Emergency, Health
Extracting “Situational Awareness” Tweets during Mass Emergency
–idea: extract clear actionable tweets related to emergency events
–look for “situational awareness”
–procedure:
1. collect data
2. annotate for situational awareness
3. annotate for linguistic features: subjectivity, formal/informal, personal dimension
4. classify for SA, linguistic features
Feature space:
–n-grams
–POS
–linguistic features

Results: classification outperforms naive baseline
linguistic features improve accuracy over unigrams but not necessarily POS

You are What You Tweet: Analyzing Twitter for Public Health
-identifying health-related keywords in Twitter

.96 correlation between flu-related tweet rate and CDC flu rate (consistent with prediction)

-used location, other tweet properties

-allergy map consistent with a priori assumptions

-self-medication data

-model not amenable to per-user analyses

Foster Provost Keynote: Social Targeting and Privacy
-Brief history of targeting:
–Contextual -> Demographic -> Psychographic -> Social
–Social targeting: choosing consumers for offers based on connections to other, specific consumers. Social network targeting is a special case of social targeting.

–Develop and expand the notion of social targeting
–present social targeting design

2 different goals for (display) advertising
–Direct response (short-term focus)
–Brand advertising (long-term focus)

In both cases we’d like to identify some “brand action” of interest, and induce consumers to take that action at a higher rate
–purchasing a product, downloading trial version, downloading product info, entering a brand contest, joining a loyalty club, visiting the brand’s website, etc.
–beware of using clicks as a brand action!
–EXTREMELY low correlation between clicking and purchasing

Social targeting will use brand actions to identify “seed” consumers and also for forward-looking and/or holdout evaluation of targeting effectiveness

Social network targeting
–cross btw viral marketing and traditional
—target “network neighbors” of existing customers
—based on direct communication between consumers
—this could expand “virally” through the network without any word-of-mouth advocacy, or could take advantage of it
—this results into 3x-5x rate lift in response

Privacy-related backlash from consumers
-are there points that give us acceptable tradeoffs between “privacy” and efficacy?
One option: doubly-anonymized bipartite content-affinity network. Bipartite network between browsers and content visited, anonymize both browsers and content. (KDD 2009)
-mode transform to create content-affinity network among browsers
-target the strong social neighbors (e.g. in ad exchanges)

Are the social neighbors “like” the seed brand actors? Some data showing yes, as would be expected by homophily

-Some brand proximity measures: number of unique content pieces connecting browser to action taker, maximum number of distinct paths, minimum euclidead distance of normalized content vector to seed node, etc.

-Make each proximity measure a feature in a predictive model, where you’re trying to predict likelihood of taking a brand action

Data:
~10mln anonymized browsers, all of their observed visits to social media content over 90 days (here: from several of the largest SN sites)
–10^7 browsers, 10^8 social media pages, 15 (mostly) well-known brands

-Turns out anonymized network embeds social network

Fast forward 1.5 years, “in vivo” performance by a marketing company. Many brands, all experienced positive lift from social network targeting, median lift was 5x. (not really comparable to lab results though)

Results improve significantly with more data

Main Points Revisited:
1. Social targeting goes beyond just social-network targeting
–define connections in various ways, such as visiting same social media pages
–find people “close to” a selected set of “seed” consumers
–find people “similar to” the seeds, based on fine-grained action data

2. Social targeting applies to brand advertising and to direct-marketing advertising

3. Surprisingly, perhaps, social targeting can be quite privacy friendly (via predictive modeling over doubly anonymized data)

What Stops Social Epidemics
-Empirical dataset = digg
-most cascades fall within the critical region for transmissibility, which is a tiny band of values – why?
-mean field model does not account for graph structure (only degree distribution), and predicts a strange value for transmissibility
-adding clustering oes not significantly improve prediction

Look at cascade model: relax assumption that infections are independent. what about repeat exposures?
-Independent model shows a strong relationship between multiple exposures and P(adoption)
-Actual relationship between multiple exposures and P(adoption) is much less strong
-changing assumption of infection to cap effect of multiple exposures at 1 creates simulations of cascade size that reflect empirical data (digg)

-observation: number of new fans falls off exponentially over time
–cascades peter out because of lack of exposue to fresh faces!
–alternative explanation to novelty turnover models

-mechanisms of propagation can affect behavioral properties such as effect of repeated exposure
–E.g. on Digg additional recommendations of a story don’t bring it up to the top of a page, whereas on Twitter RTs (and all tweets) always go directly to the top

data available: http://www.isi.edu/~lerman/downloads/digg2009.html

Differential Adaptive Diffusion: Understanding Diversity and Learning Whom to Trust in Viral Marketing
–Flickr is similar to Twitter here, as you get “latest from your friends” updates

Viral marketing: testing assumptions of independence
–Selective recommendations lead to trust imbalances between peers

Assumptions:
-Peer influence is dynamic (dependent on previous user interactions)
-Viral marketing strategies have an implicit effect on the underlying social network (sometimes changing the structure of the underlying network altogether)
-Peer influence dependent on product being spread

In existing models, influence probabilities assumed to be static, insensitive to product type, known in advance -> assumptions need to be challenged!

Case study: Digg
Following links define social network
–12K users
–1.3M follow edges
–50K news stories
–2M digg edges

-Observation 1a: most users digg stories that they are interested in / match their submission profile
-Observation 1b: a few users digg stories that are different from submissions
-Observation 1c: three kinds of digg users:
–focused on a few topics
–biased towards a few topics
–balanced (least populous group)

-Observation 2: Effect of HOmophily on Adoption
–Peers with similar topic preferences gain confidence in each other’s recommendations over time
–Peers with dissimilar topic preferences lose condifence in each other’s recommendations over time

These observations are clearly at odds with assumptions of current diffusion models

Differential adaptive diffusion:
-Influence probability between u,v for category c:
p(u,v) = w_i(v,u) x F(v,c)
–confidence of v in u at campaign i
–focus of v on category c
–Use Linear Kernel functions

Experimental evaluation:
–predict future adoptions (split dataset into halves temporally)

Baselines:
-Bernoulli: each product rec = Bernoulli Trial
-Influence probabilities using MLE over given contagion time for each user

-Bernoulli-PC:
-as Bernoulli, but each peer gets partial credit for making recommendation

Implications for viral marketing:
-Selective behavior: users will listen more to recommendations that are successful

-Introduce reward for accepted recommendations / penalty for rejected ones
–Use ABM to test
—results: intermediate level of reward lead to consistently high adoption rate & confidence over a set of marketing campaigns

-Tested for presence of spammers and found ABM adapts to presence of spammers and lowers their confidence

Participation Maximization Based on Social Influence in Online Discussion Forums
-Look at influence on user posting
-create edges u->v (interpersonal influence) and u->t (thread influence ~ topic interest)

Problem Statement:
-allocate exactly B threads to each user (e.g. sidebar)
-user will look at B threads with higher probability due to prominence
-want to optimize total number of participants

This can be interpreted as a max social welfare problem
-consider graph of users u -> threads t, want to max the number of users u who participate across all threads t

-existing approaches offer good approximation (1-1/e assuming submodularity which can be shown for this problem) but either work only for new threads or require (mn)^7 computations

-new approach
additional influence that is brought by displaying thread t user v’s sidebar = p(v is activated) * (number of users activated)

Data: TripAdvisor
-Implicit Influence network
–keep edge u->v iff v forllows u to post in at least 2 threads
–EM-algorithm to learn edge weight w_{u,v}
-Visit probabilities
–Note Random, TEABIF, TABI (algorithms used) don’t rely on visit probabilitiyes
–delta_r, drawn from empirical distribution (thread rank)

-Show that TABI performs consistently better under different numbers of threads (30, 40, 50), dfferent topics, different original visit probabilities, allocating in different time slots

Panel: Is Social Media making Media Consumption and Production Better?
Krishna Gummadi – need to understand social media-based news dissemination better

1. Who is psreading news to whom? Are grassroots really active?
2. Why are users spreading news? What incentives drive their behavior? How do media sites select stories to propagate, what drives social marketers to propagate news?
3. How is users’ exposure to news changing?

Marcus Mabry

Catherine Quayle: What about news quality? Great leveling of information brings some good things, but more bad things. New model = user incoming information from other sources, incl. other users. But now user has no context for importance / relevance / veracity

Catherine Quayle: is curating more information better?

Mor Namaan – News for the People?
Zuckerberg quote (squirrel dying in front of your house may be more important than people starving in Africa)

Does social media help news generation & consumption in a way that is an improvement to society?

“Real news – reporting done for citizens vs. consumers – is a public good” –Clay Shirky

Need an intermediate between the recluse typing on his typewriter and the bystander taking video on her phone

Tools for journalism (detecting and covering breaking events, filtering, validating information, finding and evaluating sources, help dissemination and discussion)
-existing tools (Rutgers, MIT)
-*Physical World* Events (#in10years vs. #jan25 vs. #rain)

eyewitness id and verify

“we shape our tools, and thereafter our tools shape us” –Marshall McLuhan
“Everyone can speak, but no one can be heard” -Yochai Benkler

Gilad Lotan

Attention is the Bottleneck

What is produced vs. what is consumed? (click-cloud viz)

keith urbahn tweet that triggered OSL death tweet cascade
-gaining your network’s trust

live tweeting of the OSL operation
–pakistani twitter user linked to by Forbes blogger who was searching for information about raid after Obama announcement

–what about humans translating comments on forums?

–is it more important for producers to know what to put out vs. consumers to know what to write?

–what about Wikipedia and news event coverage?
—Is the success of WP at covering the news pointing to failing of MSM at covering? Vis-a-vis obsession with latest news?

-NYT teaches online course about how to be a “news gatherer for your community”

-what is the ideal business model for news? IS there an ideal business model for news?

Summer Social Webshop
on
Technology-Mediated Social Participation
University of Maryland, College Park
August 23-26, 2011

Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers.  I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically.   The students who attended include some of the now leading researchers in the field of social science studies of the internet.  There is an impressive alumni list.

The last Webshop was held in 2003 and many years and significant changes have occurred in the time since.  Twitter, Facebook, StreetView, iPad, FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run.  Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind.  A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.

The Summer Social Webshop (@Webshop2011) is happening again!  With the generous support of the National Science Foundation and additional assistance from Google Research, this August 23-26 at the University of Maryland, College Park, a group of students will hear and engage with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives.  Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event will gather students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies.   Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines are invited to apply to attend this summer’s 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explores the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.  The workshop should be of interest to graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.

For more information, please contact Alan Neustadtl (alan.neustadtl@gmail.com).

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