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Carley, Kathleen M., and David S. Kaufer. "Semantic Connectivity: An Approach for Analyzing Symbols in Semantic Networks."

Carley, Kathleen M., and David S. Kaufer. "Semantic Connectivity: An Approach for Analyzing Symbols in Semantic Networks." Communication Theory 3.3 (1993): 183-213. [link to CASOS]


(This is the second in a series of publications I've downloaded from CASOS.)

"Density measures have also dominated the analysis of semantic networks. There is now a sizable amount of work on the generation of such networks from linguistic data. The majority of that work locates the network and displays it. When attempts are made to analyze the network, the focus is typically on the density (i.e., the number of links) of particular concepts (which serve as the nodes) within the network and on the inferences that can be made about the communicative prominence of such concepts in light of their density. While density is a useful way of analyzing the communicative “connectivity� of a symbol in a message, it provides only one dimension for analyzing connectivity within a semantic network. In this article we offer two further dimensions - conductivity and consensus - with which to analyze semantic networks for connectivity" (183).

"These techniques generate a semantic network in which concepts are the nodes and the relationships between concepts, the links. The same level of attention, however, has yet to be given to analyzing the resulting network. In general, researchers are content to display the resulting network" (184).

"The literature on the symbol has mainly taken its direction from the analysis of the literary symbol, often the literary metaphor or allegory, which are well known to elicit multiple levels of rich, often imagistic, inference. This linkage has produced a heavy bias in favor of explaining the symbol in terms of interpretive density, the sheer number of continuous connections that the symbol makes available to the understanding (as, for example, in the world is a stage)" (185).

"Density, however, is not the only primitive constitutive of symbolic connectivity. A second and independent primitive is consensus. Some symbols function as such only because they are connected to historical inferences that are widely shared. A symbol like 1492 has relatively low density for a grammar school student but nonetheless performs a symbolic function because it draws on beliefs that are almost universally shared across that population" (185).

"Elliptical expressions combining density with consensus were known in ancient rhetoric as enthymemes" (186). (Also, e.g., slogans, stereotypes, cliches)

"There is a third primitive constitutive of symbolic expression, one that can combine with the primitives of density and consensus but can also stand on its own. We call this primitive conductivity. Conductivity is the capacity of an expression in context to carry (or trigger) information in a two-directional flow. Information flows in two directions when it both triggers and is triggered by other available information in the context....The importance of a word known only for its conductivity (and so lacking in density or consensus) is not the expression itself but rather the flow of ideas it keeps stimulating" (186).

"The primary example of such a purely conductive symbol is the buzzword....The “meaning� of a buzzword lies not in its direct or immediate denotation but in the elaborations that everyday users have come to give it" (186).

Measuring "Three Dimensions of Connectivity in a Semantic Network"

Density: "The density of a focal concept is the number of concepts to which the focal concept is directly linked, regardless of the direction of the link" (187).

Conductivity: "The total conductivity of a focal concept is measured by multiplying the number of concepts directly linked into it by the number of concepts directly linked out from it....Note also that a focal concept acquires density and conductivity in a network at a very different rate. Density grows one concept at a time, additively. Conductivity grows faster than that, multiplicatively" (189).

Consensus: "We can compute consensus by surveying or sampling the agreements of language users about which concepts are connected to which on a pair-by-pair basis. The more highly consented to links to or from the focal concept, the higher its consensus" (189).

consensus measured by threshhold: "The use of a threshold does not imply the absence of agreement for links that fall below it; it only implies the absence of “social knowledge.� Social knowledge consists of that information that is more or less known by most individuals in the society....Threshold-setting for social knowledge is important because the level of agreement required to achieve social knowledge may vary by context" (190).

A Typology of Semantic Categories Formed by the Intersection of These Dimensions

Assuming that focal concepts can be either "high" or "low" on the 3 dimensions:

ConnectivityConsensusLanguage Category
LowLowLowOrdinary Words

Qualifications -- will vary from community to community, and also over time. None of these dimensions is necessarily stable over the long term or across groups (196).

Applied to 3 semantic environments: residence hall, writing classroom, thesaurus entry (the first two studies are published elsewhere, the 3rd original to the article).

"On occasion, we want to make comparative inferences about connectivity across topical contexts. For example, in comparing scientific disciplines one might wish to test the proposition that disciplines associated with physical as opposed to social reality produce shorter articles in part because the language of such disciplines is less amorphous and there is greater consensus as to what words mean" (199).

Application domains: analyzing argument discourse (207), decision making and voting (207), classroom learning (208), lexical choice (209).


Of all the CASOS essays, this one may provide the best starting point for getting at a full picture of what the network analysis of texts can accomplish. First, the typology outlined above provides a very persuasive account of the spectrum from ordinary language to symbols, one that includes measurable features yet still generates some interpretive leverage.

One thing that occurs to me as I look back through it is the relative thinness of representations like tagclouds, measuring as they do the density of terms in a dataset. This has got me thinking about how the other dimensions might likewise be represented.

Oh, and second, the math here is not overwhelming, although it's still a bit of a challenge for me. I get the impression that I could manage it if I were interested in doing this kind of project. Having three different kinds of environments represented also allows C&K to offer some different examples of hypotheses that they can test given the data here.