The Second Workshop on Information in Networks September 24-25, 2010, New York City Sponsored in part by the Initiative on Information in Networks Organizers: Sinan Aral, Foster Provost, Arun Sundararajan The second Workshop on Information in Networks (WIN10) will be…
The book Analyzing Social Media Networks with NodeXL: Insights from a connected world is now available [Amazon] from Morgan-Kaufman. Co-authored by Professor Derek Hansen (College of Information Studies) and Professor Ben Shneiderman (Computer Science/Human Computer Interaction Lab) from the University of Maryland and Marc Smith from Connected Action, the book is a introduction and guide to the application of social network analysis to social media. The introductory chapters introduce the history and concepts of social network analysis an the varieties of social media, highlighting the presence of a common data structure, the network, in otherwise diverse social media systems including email, Twitter, Facebook, the WWW, Wikis, Blogs, flickr, an YouTube. The central section of the book reviews a step-by-step guide to using the key features of NodeXL, the free and open social media network analysis add-in for Excel 2007 and 2010. Readers can move from simple hand entered networks of a few nodes up to complex graphs extracted from a variety of social media services. The remainder of the book are focused chapters dedicated to analyzing the networks found within a specific social media service. These chapters were contributed by leading social media researchers and illustrate the insights that can be extracted from the otherwise disorganized stream of messages, tweets, posts, comments, links, likes, tags, friends, follows, mentions, replies and ratings. A recent article about the book can be found on the Morgan-Kaufmann website.
Table of contents…
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
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
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.
-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)