Decoding Deliberation

Jennifer Stromer-Galley
University at Albany, SUNY

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     Last modified: May 14, 2005

Abstract
The literature on deliberation has grown at a rapid pace in the past ten years. The number of creative and influential experiments on deliberation have found that deliberation is beneficial (Cappella, Price, & Nir, 2002; Fishkin, 1991, 1995; Luskin, Fishkin, & Iyengar, 2004; Gastil & Dillard, 1999; Price, Cappella, & Nir, 2002). Ryfe’s (forthcoming, 2005) comprehensive review of the deliberation literature indicates, however, a large hole in the research concerning what transpires when people “deliberate.” Few studies measure how deliberative a group’s interaction is. This exploratory project aims to fill that gap. A coding scheme for content analysis is offered that is designed to identify how deliberative group discussion is. Deliberativeness in this study is grounded both on ideals of deliberation (for example, Habermas (1962/1989) and Schudson (1997)) and on prior studies that have content analyzed group discussion (Dahlberg, 2001; Davis, 1999; Dutwin, 2002; Graham & Witschge, 2003; Hill & Hughes, 1998).

Yahoo!’s Politics chat room dialogue is used as the data source for this exploratory project. Coding is done at the level of the utterance. The content analysis includes measuring whether the discussion is interactive and invites people into the discussion, provides facts, opinions, and reasons for opinions, provides the source upon which opinions are based, expresses agreement or disagreement, and whether there is personal attack. It also focuses on the type of utterances in the chat, that is, what the utterance is doing--as understood in speech act theory. They type of utterance is coded into one of five possible categories: assertion, directive, expressive, commissive, or performative (as adapted from Cooren (1999) and Twitchell, Adkins, Nunamaker, & Burgoon, (2004)).

Preliminary results suggest that the coding scheme differentiates between rich and poor deliberation along multiple dimensions and provides a basis for understanding how groups maintain or fail to create quality deliberation.

Works Cited
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Twitchell, D. P., Adkins, M., Nunamaker, J. F., Jr., & Burgoon, J. (2004). Using speech act theory to model conversations for automated classification and retrieval. Paper presented at the Proceedings of the 9th International Working Conference on the Language-Action Perspective on Communication Modeling, New Brunswick, NJ.

Appendices
      Codebook for Decoding Deliberation