get_component_summary: get_component_summary

In LeonardoSaracino/CooRnet_projectSNA: Detect coordinated link sharing behavior on social media

Description

A function to get summary data by coordinated component

Usage

get_component_summary(output)

Arguments

output

the output list resulting from the function get_coord_shares

Details

The gini values are computed by using the Gini coefficient on the proportions of unique domains each component shared. The Gini coefficient is a measure of the degree of concentration (inequality) of a variable in a distribution. It ranges between 0 and 1: the more nearly equal the distribution, the lower its Gini index. When a component shared just one domain, the value of the variable is set to 1. It is calculated separately for full_domains (e.g. www.foxnews.com, video.foxnews.com) and parent domains (foxnews.com)

The cooRscore.avg is a measures of component coordination. Higher values implies higher coordination. Its value is calculated by dividing, for each entity in a coordinated network, its strength by its degree, and then calculating the average by component of these values.

The cooRshare_ratio.avg is an addional measure of component coordination ranging from 0 (no shares coordinated) to 1 (all shares coordinated).

Value

A data frame containing summary data by each coordinated component: the average subscribers number of entities in a component, the proportion of coordinated shares over the total shares (coorshare_ratio), the average coordinated score (avg_cooRscore), a measure of dispersion (gini) in the distribution of domains coordinatedly shared by the component (0-1). Higher values correspond to an higher concentration (less different domains linked), the top 5 coordinatedly shared domains (ranked by n. of shares), the total number coordinatedly shared of domains

Examples

  # get the top ten posts containing URLs shared by each network component and by engagement
  component_summary <- get_component_summary(output)

  # clustering the components rowwise mutate
  clusters <- hclust(dist(component_summary[, 2:4]))
  plot(clusters)

LeonardoSaracino/CooRnet_projectSNA documentation built on Dec. 18, 2021, 4:33 a.m.