Predicting online participation through Bayesian network analysis
Fig 3
Directed acyclic graphs of the relations between factors associated with participation in online activism.
Source: [35]. N = 27 379 individuals in 19 countries. Notes: Within Bayesian network analysis, score-based Tabu and hybrid H2PC algorithms were applied to analyse the data and learn the structure of the causal relations between the variables. Dashed blue lines represent false positives, i.e., edges that are not present in the structure learned by the Tabu algorithm but present in the structure learned by H2PC. Orange lines represent false negatives, i.e., edges that are present in the structure learned by the Tabu algorithm but absent in the structure learned by H2PC. The direction of each arc represents the orientation of the causality between two variables: e.g., in a relationship A → B, A is a parent and B is its child. All the edges from the other nodes to “Age”, “Gender” and “Born in the country” were blacklisted prior to learning the structure. No other edges were blacklisted. In the figure, those nodes that can only be parents have a darker blue color. The node “Country” (i.e., the country of the respondent’s residency) is present in the structure but not depicted by the figure to facilitate the apprehension of the relations between the nodes of interest. All variables are individual-level variables.