Causal modeling and attribution

A computing device comprising:

• a memory configured to maintain input data that is received as a representation of social media interactions between users of social media;
• and
• a processor system to implement a causal modeling application that applies a dynamical causal modeling framework that is configured to:
• receive communications between users that are modeled as text data, the text data including a sentiment expressed by one or more of the users about a subject in the communications;
• analyze the text data to identify the sentiment about the subject, the analyzing includes calculating a weighted average of one more sentiment scores associated with the sentiment of the subject in the communications;
• generate input data based on the weighted average of the one or more sentiment scores associated with the sentiment of the subject in the communications;
• determine causal relationships between the users based in part on the input data that represents the social media interactions and simultaneous modeling of one or more influence variables such that the simultaneous modeling incorporates random fluctuations associated with the one or more influence variables;
• determine one or more influence variables from the one or more influence variables that influence the causal relationships between the users, the one or more influence variables including one or more endogenous variables and one or more exogenous variables, the one or more exogenous variables moderating influence of the endogenous variables on the causal relationships between the users;
• generate a causal relationships model based on the influence variables and the causal relationships between the users;
• and
• controlling an instance of content based on the causal relationships model.

A computer-readable storage media comprising a causal modeling application stored as instructions that are executable and, responsive to execution of the instructions by a computing device, the computing device performs operations comprising:

• receiving communications between users that are modeled as text data, the text data including a sentiment expressed by one or more of the users about a subject in the communications;
• analyzing the text data to identify the sentiment about the subject, the analyzing includes calculating a weighted average of one or more sentiment scores associated with the sentiment of the subject in the communications;
• generating input data based on the weighted average of the one or more sentiment scores associated with the sentiment of the subject in the communications;
• receiving input data as a representation of communications between users of social media;
• determining causal relationships between the users based in part on the input data that represents the communications and simultaneously modeling of one or more influence variables such that the simultaneous modeling incorporates random fluctuations associated with the one or more influence variables;
• determining one or more influence variables from the one or more influence variables that influence the causal relationships between the users, the influence variables including one or more exogenous variables and one or more endogenous variables, the one or more exogenous variables moderating influence of the one or more endogenous variables on the causal relationships between the users;
• generating a causal relationships model based on the one or more influence variables and the causal relationships between the users;
• and
• controlling an instance of content based on the causal relationships model.