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NodeXL Image in NewScientist: Paper trail: Inside the stem cell wars

The June 9 issue of New Scientist contains an article featuring a map of scientific citation that was generated by NodeXL. The article about the patterns of citation and the time to publication for US and non-US based scientists contains a map of which papers and authors cite which other papers and authors. From New [...]
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June 3 and 4, 2010 – Personal Democracy Forum 2010 – NYC

I spoke on June 4th at the Personal Democracy Forum in New York City about what social media network maps can tell us about various political figures and topics. The video is available here: http://streams.civicolive.com/stream/127/5040/6000. Political discussions are obviously a major area of social media use. This talk explores the ways social network analysis and visualization [...]
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May 28th, 2010 – Workshop: Government Applications of Social Media Networks and Communities, University of Maryland, Human Computer Interaction Lab (HCIL)

http://www.cs.umd.edu/hcil/soh/ The HCIL Government Applications of Social Media Networks & Communities Workshop, as part of the 27th Annual Human Computer Interaction Lab (HCIL) Symposium, at the University of Maryland, examined how social media can be systematically applied to increase civic participation on national priorities. When: Friday, May 28, 2010, 9:30am-4:00pm Where: CSIC Building, UMD, College Park Who: Government thought [...]
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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 [...]
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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 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. [...]
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