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.

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.

Women and men communicate differently in both face-to- face and computer-mediated environments. We study linguistic patterns considered gendered in reviews contributed to the Internet Movie Database. IMDb has been described as a male-majority community, in which females contribute fewer reviews and enjoy less prestige than males. Analyzing reviews posted by prolific males and females, we hypothesize that females adjust their communication styles to be in sync with their male counterparts. We find evidence that while certain characteristics of “female language” persevere over time (e.g., frequent use of pronouns) others (e.g., hedging) decrease with time. Surprisingly, we also find that males often increase their use of “female” features. Our results indicate, that even when they resemble men’s reviews linguistically, women’s reviews still enjoy less prestige and smaller audiences.

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 cannot be correlated with peripheral locations in a network. These indicate that Twitter plays a more nuanced role in political communication than previously expected.