Abstract: A system for generating and analyzing organizational influence data is disclosed. In one embodiment, at least one processor is configured to access first data associated with a plurality of policymakers; generate first nodes representing the plurality of policymakers within an issue graph model; generate a second node representing an organization; receive a selection of an agenda issue of interest to the organization; access second data associated with the organization; generate links within the issue graph model representing relationships between the first nodes and the second node; determine an organizational influence factor comprising a measure of how likely the second node is to affect a property of each of the first nodes; identify at least one node of the first nodes associated with the selected agenda issue based on the organizational influence factor; and output node properties associated with the identified node.

Abstract: A method for identifying stakeholders relative to an issue is disclosed. In one embodiment, the method may include accessing first data associated with a plurality of individuals associated with an organization; generating first nodes representing the plurality of individuals within an issue graph model; accessing second data associated with one or more policies; generating second nodes representing the one or more policies within the issue graph model based on the second data; receiving an indication of a selected agenda issue; generating links within the issue graph model representing relationships between the first nodes and the second nodes; determining importance scores for the first nodes in the issue graph; identifying a node of the plurality of first nodes associated with the at least one selected agenda issue based on the importance scores; and outputting node properties associated with the identified node.

Abstract: An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages.

Abstract: An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages.

Abstract: An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include or use in creating messages for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes.

Abstract: An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include or use in creating messages for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes.

Abstract: An Internet-based agenda data analysis system may include at least one processor configured to maintain a list of user-selectable agenda issues, present to a user via a user interface, the list of user-selectable agenda issues, and receive via the user interface, based on a selection from the list, agenda issues of interest to an organization. The processor may be configured to access information scraped from the Internet to determine, for a plurality of policymakers, individual policymaker data from which an alignment position of each policymaker on each of the agenda issues is determinable, calculate alignment position data from the individual policymaker data, the alignment position data corresponding to relative positions of each of the plurality of policymakers on each of the plurality of selected issues, and transform the alignment position data into a graphical display that presents the alignment positions of multiple policymakers.