After data cleanup, this is a first round of network visualization and analysis.
The Czech corpus on 2022-09-07 is coded with 586 codes, giving rise to 14,455 co-occurrences. The stacked CCN has 6,397 edges.
47 codes have the highest core value, k = 46. They are:
codes with in the highest-K *k*-core (*k* = 46)
*healthcare system *misunderstanding (culture or value based) Adolf Hitler Andrej Babiš anti-COVID measures anti-vaxxers caring about future generations conspiracy theories crisis DDeliteestrangement DDpassel Donald Trump dystopia Ecorrupt Ehightax elections employer Epoverty family structures fear financial interests hidden agenda impact of COVID-19 information overflow fatigue Joe Biden LAinadequate LAinsecurity LAretirement lockdown media migration Miloš Zeman Politicians public interest respirators/face masks retired people SAinciv satisfaction schools sense of threat SIincompetence socialisation uncertainty voting Václav Havel young generation Z COVID-19 - Category
A network’s Simmelian backbone is a subset of its most redundant edges. Edges are redundant when they conect two nodes that have many neighbors in common. They are used to extract community structure from dense networks, such as CCNs.
In this particular CCN, quite soon the “hairball” structure resolves into three distinct communities, two very dense smaller hairballs, one less so. Above I show a visualization for r > 30, with 108 codes and 1,581 stacked edges. The two dense communities are: one, that I will call the “southwest community” because of where it appears in the visualization above. Its component nodes are 40 with 799 edges, and include many of the codes in the highest-k K-core. Of course, if I choose a threshold value of r higher than 30, all communities will have fewer codes and edges; conversely, if I lower my threshold r they will have more. But this structure is quite stable across a good range of threshold values.
codes in the southwest community with r = 30
*healthcare system *misunderstanding (culture or value based) Adolf Hitler anti-COVID measures caring about future generations conspiracy theories crisis DDeliteestrangement Donald Trump dystopia Ecorrupt Ehightax elections employer Epoverty fear financial interests information overflow fatigue Joe Biden LAinadequate LAinsecurity LAretirement media migration Miloš Zeman Politicians public interest respirators/face masks retired people SAinciv satisfaction schools sense of threat SIincompetence uncertainty voting Václav Havel young generation Z COVID-19 - Category
The community of codes to the “nortthwest” has 30 codes and 398 edges.
codes in the northwest community with r = 30
*psychological well-being anti-COVID measures bad strategy Clubhouse dating (romantic) DDpassel Dominik Feri EDnescience family structures gender role models GENinequality high school graduation home office impact of COVID-19 information flow institutional failure labour meeting new people moral integrity online sphere online teaching Politicians protests public pressure Roman Prymula SAsupdef social isolation social media socialisation Work experience abroad
The two are connected by
impact of COVID-19,
Finally, there is a much lesse dense “eastern” community of 30 codes, connected by 103 edges. These are:
codes in the eastern community with r = 30
children Civic democratic party civil disobedience communism confusing measures covid conflict CULdisorient CULman DDpolcor democratic elections drastic measures EDprogramin Einequality grassroots movement IHphysical infection rate Jana Bobošíková LAunemployment Lubomír Volný MIGinmigra Pirate party populism post-socialist transformation sanitary-epidemiological station SPD staying at home vaccinations vaccine injury virus testing Volný blok working patterns ČSSD
The eastern community is connected to the other two via
In this CCN, the association depth d and the association breadth b of edges are correlated positively, but not tightly, with a correlation coefficient of 0.53. The very deepest edge connects ``impact of COVID 19
andonline teaching` (d = 68). This network connects 22 codes with the 34 deepest edges in the whole corpus. Each of them has d >= 21.
Below, a broader visualization, with 53 nodes and 99 edges with d >= 14.
The broadest edge of all connects
anti-vaxxers (b = 20). Three more edges have a b > 15, and they form a chain, connecting
Andrej Babiś to
SIincompetence. Recall that b is the number of individual informants that have associated these codes at least once. The network below shows the 44 broadest edges, each supported by at least 7 informants. They connect 32 codes.
This is what we happen when we filter in edges with b >= 4. There are 59 codes, with 110 edges.