In summary, our data indicate that

• 14% of iSchool graduates are placed in tenure-track positions at iSchools
• 40% of iSchool graduates are placed outside academia
• Less than 50% of the tenure-track faculty hired by iSchools graduated from iSchools

Both mechanisms, placement rank and eigenvector centrality, are based on the position of schools within the network of hires and graduates. Placement rank (PR) is based on the raw number of iSchool graduates who were placed in other iSchools. iSchools that place many grads in other iSchools will have high PR. Eigenvector centrality (EC) is similar to placement rank in that a school’s EC depends upon whether its graduates are hired by other iSchools. However, EC also considers the network position of the schools doing the hiring; for instance, if School A’s graduates get hired by highly-ranked School B, then School A’s EC will increase.

These ranking systems help satisfy our natural curiosity about where institutions stand in relation to one another but also provide (1) measures of the quality of graduate education in iSchools and (2) data to inform the iSchool identity discussion.

Our Process

We used three rounds of coding to develop a robust coding scheme for the action taken in tweets. The resulting scheme used six codes – narrating, positioning, directing to information, requesting action, giving thanks, and other – to categorize the kind of action taken in a tweet. Codes were not mutually exclusive meaning a tweet could be coded as exhibiting more than one action. For example, “With massive debt, why are taxpayers funding wine tasting? Washington’s spending addiction continues http://t.co/2QaYJmo,” a tweet from Jim DeMint, was coded as both positioning and directing to information. We calculated Cohen’s kappa scores for each code and found very strong agreement between coders. The code definitions, examples, and kappas are in the table below. Positioning and directing to information were by far the most common actions exhibited on Twitter. Most of the differences between our results and Golbeck et al.’s lie in our distinctions between positioning statements and information statements.

What’s Next

We have a couple working papers about the results of our action coding; please email me if you’d like to read them. Next, we’re coding for the manner in tweets in order to understand the tones tweeters use and whether they relate to other aspects of the tweeters’ communication or offline behaviors.

Everyday Politics: Engaging Chicago Politicians on Twitter

This paper investigates the use of Twitter, a microblogging and social network service, by local Chicago politicians. Twitter provides a public communication medium in which constituents and their representatives can have public conversations that others can witness and record. We collected over 1800 tweets posted by or mentioning Chicago Aldermen or Mayor Rahm Emanuel over the summer of 2011. Using qualitative and social network methods to examine conversations between Chicagoans and representatives in city government, we present data about the content and manner of the tweets as well as the networks formed through social media participation. Through an examination of the users’ “mentioning” behaviors, our analysis indicates that Chicago’s Aldermen and Mayor use Twitter for social conversations more often than political ones and that just two of Chicago’s aldermen dominate the social media conversation. Examining their social media conversations enables us to explore how the city’s politicians frame their issues, position themselves as community leaders, and engage with their constituents.

Going ‘Bald on Record’: Relationships Among Public Officials’ Social Media Behavior and Language Use

Public officials use polarizing language – supporting language for one’s self versus pejorative language for others – as a means of establishing clear boundaries on certain issues. This has been explored to some degree in terms of how such language is conveyed in the traditional media, but minimal research has been done with regard to the role of polarizing language within social media. This paper explores how elected U.S. officials use potentially polarizing language (“civility,” “politeness,” and related forms) to draw in supporters. We analyze the content and behavior of more than 30,000 tweets from the available Twitter accounts of each elected member of Congress, particularly in terms of the nature (size and party composition) of Twitter networks for officials who use polarizing language. Network analysis via Network Workbench and NodeXL confirms that officials’ use Twitter for much more than broadcasting, officials’ interaction networks differ from their follower/friends networks, and polarizing language is not correlated with peripheral locations in a network. These indicate that Twitter plays a more nuanced role in political communication than previously expected.

When you compare this network to the bloggers Adamic and Glance studied (scroll down), you’ll notice relatively more connections between the two camps but a similarity in density – Republicans mention each other more often than do Democrats. What these mentions entail, and who’s doing the connecting are good topics for future posts. Matt Shapiro, Jahna Otterbacher, our students, and I are actively working on more results and analysis about Congress on Twitter and what their activities there mean for their political behavior and civic engagement broadly.