Emergent Pairings Between Posts

This graph represents degrees of similarity between pairs of posts, based on comparisons with respect to their disciplinary approaches and the outlooks they took on themes. (See the Explanation of Approaches and Explanation of Themes pages to understand the basis of the comparisons.) The linkages are emergent in the sense that they were generated algorithmically from a listing of the approaches and outlooks on themes taken by each post; they reveal an aspect of the structure of the set of posts that literally emerges at the level of the collection from those individual characteristics. The links were derived using a simple social network analysis of the posts--explained below.

Social Network Analysis of the Posts

The collection of posts constitute a social network in the sense that posts can have certain characteristics in common. A pair of posts which have one or more common characteristics are therefore linked, and the linkages among all the posts constitute a network. Social network analysis is the technique used to determine the full set of linkages from data about each post individually. The analysis performed here is very straightforward; it is based on the extremely accessible explanation of the technique by Kieran Healy, Using Metadata to Find Paul Revere (suggested to Zev Trachtenberg by Andy Halterman).

The characteristics of our posts used in this analysis were the approaches and outlooks on themes each took. These characteristics are presented in the legend at the end of the main text of each post; this legend can be interpreted as showing the posts's relevant metadata. The Approach and Theme Views visualize those metadata: the links from each post node show the approaches and outlooks on themes it takes, and the text associated with the approach and themes nodes shown in those views explains each characteristic.

The following table lists the characteristics for each post, indicated by a dot in the respective column (In lieu of a legend for the column headings: hover on the letter to see the full title of that characteristic; click to see a summary description in the Comments (below the graph, to the right).

D N I P Ax An Hc Hr Lm Lp Fh Fd

Posts that have dots in the same columns are similar in those respects; counting the number of such dots gives an index of the degree of similarity. Healy describes the straightforward technique for calculating the degree of similarity across the whole set of posts. The table below shows those results: the number at the intersection of a row and a column is the number of characteristics the two respective posts have in common--the higher the number the more similar the posts (on this operationalization of similarity). (The technique involves representing the table above as a matrix where the dots are replaced by 1's and the blank cells by 0's; transposing the matrix; and then multiplying those two matrices. That is the operation that produced the table below--the cells to the lower left had values corresponding to the cells on the upper right, so are left blank.)

AC1 2 1 1 1 1 1 2 1 1 1 2 1 1 1 2
AC2 2 3 2 2 1 1 0 1 2 1 1 2 0 1
AR1 3 2 2 1 1 1 1 2 1 1 3 0 2
AR2 2 2 1 1 1 2 2 1 1 3 0 2
IS1 4 2 2 1 0 3 2 1 2 1 2
IS2 2 2 1 0 3 2 1 2 1 2
KG1 2 0 0 1 2 0 1 1 2
KG2 1 1 1 4 0 1 1 2
MW1 3 1 1 1 1 1 2
MW2 0 1 1 1 1 2
LS1 1 1 2 0 1
LS2 0 1 1 2
NT1 2 1 1
NT2 0 2
ZT1 2

The network graph was produced by using the values in the second table as the factors that determine the width of the edges between the respective post nodes. For example, follow the KG2 row to the LS2 column to see that those posts have a degree of similarity of 4--the highest possible--and on the graph the edge between them is as wide as possible.