Name ordering is clear, so what about the clustering and similarity views?
Cluster view: The newspapers with the same color on the diagonal is a cluster. For example (posta, hurriyet, milliyet) is a cluster and (yenisafak, Haber7, AkitGazetem) is another one. So, our methodology seems to be doing pretty well, huh? The clustering algorithm measures how well a network is decomposed into modular communities. Our graph G=(V,E) is a weighted clique where set of V is the 35 newspapers and W(E) is the pairwise similarities. 11 clusters visualized in this matrix are generated based on modularity measure.
Similarity view: As the newspapers appear more to the right/down on the axis, they are (cumulatively) less similar to others, i.e. followers of these newspapers are isolated, or these newspapers may be called as marginals.
More on this project: My blog post on Turkish Media Analysis Exploiting Twitter. [EDIT] Added Reader Network of Turkish News Media.