ICWSM 2010 Liveblog, Day 3

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

Michael Kearns Keynote

Experiments: Graph Coloring / Consensus / Voting

Topology of the Network vs. what was the network used for?

Voting experiments – similar to consensus, with a crucial strategic difference.

Introduce a tension between:

-Individual preferences

-Collective unity

-Color choices; challenge comes from competing incentives

Red, blue. People unaware of global network structure

Payoffs: if everyone picks same color w/in 2 minutes, experiment ends, and everyone gets some payoff. But different players have different incentives (e.g. I may get paid p if everyone converges to blue, but 2p if everyone converges to red). If there is no consensus, nobody gets a payoff

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ICWSM 2010 Liveblog, Day 2

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

***Microblogging 2***

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (Tumasjan et al.)

Successful use of social media in las presidential campaign has established twitter as an integral part of political campaign toolbox

Goal: analyze on Twitter: 1. Deliberation, 2. Sentiment, 3. Prediction

Previous work:

Deliberation: Honeycutt and Herring – Twitter not only used for one-way comm, but 31% of all tweets direct a specific addressee. Kroop and Jansen – political internet discussion boards dominated by small # of heavy users

Sentiment: How accurately can Twitter inform us about the electorate’s political sentiment?

Prediction: can Twitter serve as a predictor of the election result?

Data: examined more than 100k tweets and extracted their sentiment using LIWC

Target: German federal election 2009

Results:

1. While Twitter is used as a forum for political deliberation on substantive issues, this forum is dominated by heavy users

Two widely accepted indicators of blog-based deliberation:

-The exchange of substantive issues (31% of all messages contain “@”),

-Equality of participaion: While the distribution of users across groups is almost identical with the one found on internet message boards, we find even less equality of participation for the political debate on Twitter. Additional analyses have shown users to exhibit a party-bias in the volume and sentiment of messages.

2. The online sentiment in tweets reflects nuanced offline differences between the politicians in our sample.

LIWC profiles:

-Leading candidates: Very similar profile for all leading candidates, only polarizing political characters, such as liberal leader and socialist, deviate in line with their roles as opposition leaders. Messages mentioning Steinmeir (coalition leader) are most tentative

3. Similarity of profiles is a plausible reflection of the political proximity between the parties

Key findings: high convergence of leading candidates, more divergence among politicians of governin grand coalition than among those of a potential right wing coalition

4. Activity on Twitter prior to election seems to validly reflect the election outcome (MAE 1.65%), and joint party mentions accurately reflect the political ties between parties.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (Brendan O’Connor)

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ICWSM 2010 Liveblog, Day 1

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

We will be liveblogging (when possible) from ICWSM 2010, going on now!

Keynote: Bob Kraut, CMU

ICWSM 2010 - Bob Kraut
implications for community design
-offline theories of socialization helpful, not definitive
-online communities can build in good socialization practice
-e.g. WP welcoming committee
Two Types of Commitments to Groups
-identity based groups
-bond based groups
Added Identity & Bond Features to MovieLens
Introduced Subgroups into MovieLens
Identity features that focus on subgroups
Individual profiles
bond-based design:+11% logins
identity-based design:+44% logins

Social Media Talks at Aachen University, December 7th & 8th

University of Aachen

After visiting the Oxford Internet Institute on December 4th, I will be visiting Aachen University to talk about social media network analysis this December 7th and 8th.

I will be visiting with my hosts, Zinayida Petrushyna and Ralf Klamma who have been doing insightful work exploring social media sites like Media Wikis and Wikipedia.  The Wiki Watcher project is a big favorite of mine.  Their working group on Social Network Analysis on Dynamic Digital Networks is of particular interest.

While I am back in Aachen, I hope I will get to see some of the folks I met at the “European Microsoft Innovation Center” (EMIC) located there.  They focus on research into embedded systems which I see as having vast implications for the ways computers sense social relationships.  When sociologically aware machines are widely used, we will have new ways of building and maintaining relationships.

I will give three presentations over two days in Aachen – I hope you can attend if you are in the area!

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Monday, December 7, 2009 (in cooperation with the Web Science II course at RWTH Aachen University):

12:15 – 13:45 Social Media Visualization Analysis: overview of work to visualize social media using treemaps, histograms, scatterplots and social network diagrams

Using social media makes evidence of social relationships into machine readable data streams.  The resulting data sets contain time series, hierarchy, and network data structures that can be visualized to illustrate the range of variation in social media data spaces and participants.  Histograms, line charts, treemaps, and network diagrams can be used in concert to illuminate the many facets of behavior and population present in social media spaces.  Resulting images illustrate the range of variation of individual and collective formations in social media spaces.

16:00 – 17:30 NodeXL – A hands-on guide: a workshop style review of sample data sets that are processed through NodeXL operations to generate metrics and a visualization that tells a story.

As social media networks proliferate there is a growing need for tools to manage, analyze, and visualize network graphs.  NodeXL is an add-in for Office 2007 that provides social network diagram and analysis tools in the context of a spreadsheet application. Adding the directed graph chart type to Excel opens up many possibilities for easily manipulating networks and controlling their display properties. In this workshop the steps needed to install and productively operate NodeXL for Office 2007 are reviewed. The free and open NodeXL add-in provides directed graph charting features within Excel, allowing users to create node-link diagrams with control over each node and edge color, size, transparency and shape without requiring the use of a command line interface or programming language. Since NodeXL builds within Excel, all of the controls and programmatic features of Office are available. Additional features of NodeXL generate 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. This session will provide a walk through the basic operation of NodeXL.

Tuesday, December 8, 2009

10:00 – 11:00 In the future my phone will notice your phone: from ephemeral to archival societies

New sources of data from everyday life are being captured and recorded with mobile devices, creating a new stream of archival material that is richer than all but the most obsessively observed biographies. Many organizations are adopting social media and creating data sets that map their internal social network structure as an accidental by-product.  Studying these data is sets is a focus of growing interest. Research projects like SenseCam are now becoming products and services like nTag, Spotme, Fire Eagle, and Google Lattitude using devices like iPhone and G1 are weaving location into every application.   When my phone notices your phone a new set of mobile social software applications become possible that capture data about other people as they beacon their identifies to one another. Additional sensors will collect medical data to improve our health and safety, as early adopters in the “Quantified Self” movement make clear.  Social media systems are being linked to one another to enable cascades of events from a single message as status updates are passed among Facebook, LinkedIn, Twitter, and blogs automatically aggregate the results of searches and post articles that themselves may trigger other events.  Taking a photo or updating a status message can now set off a series of unpredictable events.  The result will be lives that are more publicly displayed than ever before.  Add potential improvements in audio and facial recognition and a new world of continuous observation and publication emerges.  Some benefits, like those displayed by the Google Flu tracking system, illustrate the potential for insight from aggregated sensor data.  Risks include more efficient state and corporate surveillance and self-imposed censorship.