Abstract: A text analytics system may predict whether a policy will be adopted. The system may include at least one processor configured to access information scraped from the Internet to identify text data associated with comments expressed by a plurality of individuals about a proposed policy. The at least one processor may be further configured to analyze the text data in order to determine a sentiment of each comment; apply an influence filter to each comment to determine an influence metric associated with each comment; weight each comment using the influence metric; determine based on an aggregate of the weighted comments, an indicator associated with adoption of the policy; and transmit the indicator to a system user.

Abstract: A system creates alerts of issues of importance to an organization. While an organization does not want to miss the opportunity to run an advocacy campaign on an issue of importance, it also does not want to run an unsuccessful campaign that might burden or bore those to whom the campaign is directed. The system maintains a history of previous campaigns as well as success outcomes of those campaigns. A computational model operates on selected previous campaigns and a candidate issue to determine a score indicative of whether a campaign should be run. The score can include a combination of relevancy to criteria for an issue of importance, similarity to issues from previous campaigns, and outcome success data for the selected previous campaigns. If the combined score meets a threshold, the system can present an option to initiate a new advocacy campaign on the candidate issue.

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: A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

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: A system creates alerts of issues of importance to an organization. While an organization does not want to miss the opportunity to run an advocacy campaign on an issue of importance, it also does not want to run an unsuccessful campaign that might burden or bore those to whom the campaign is directed. The system maintains a history of previous campaigns as well as success outcomes of those campaigns. A computational model operates on selected previous campaigns and a candidate issue to determine a score indicative of whether a campaign should be run. The score can include a combination of relevancy to criteria for an issue of importance, similarity to issues from previous campaigns, and outcome success data for the selected previous campaigns. If the combined score meets a threshold, the system can present an option to initiate a new advocacy campaign on the candidate issue.

Abstract: A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

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: A prediction system provided with an integrated communications interface may include at least one processor configured to scrape the Internet to identify a currently pending legislative bill and information about legislators slated to vote on the pending bill. The processor may parse the information to determine a tendency position for each legislator. The processor may transmit for display to a system user a virtual whipboard that groups legislators into a plurality of groups based on determined tendency positions. The processor may receive a selected one of the plurality of groups of legislators for a communication interaction based on the determined tendency position of the group and access a legislator database that includes legislative communication addresses of legislative personnel scraped from the Internet and divided into a plurality of legislative function categories and receive from the system user a selection of at least one of the plurality of legislative function categories.

Abstract: A hybrid prediction system may aggregate electronic data to identify and initially predict an outcome of a future event and subsequently update the initial prediction. The system may include at least one processor and a memory. The processor may access data scraped from the Internet. The data may be associated with at least one future event. The processor may further store the scraped data, determine, from the scraped data, an initial prediction of the outcome of the at least one future event, generate, from the scraped data, an initial likelihood indication associated with the initial prediction, and transmit the initial prediction and the initial likelihood indication to a device associated with one or more users. The processor may further receive proprietary information, store the proprietary information, determine, using the scraped data and the proprietary information, a subsequent likelihood indication, and transmit the subsequent likelihood indication to the device.

Abstract: A prediction system provided with an integrated communications interface may include at least one processor configured to scrape the Internet to identify a currently pending legislative bill and information about legislators slated to vote on the pending bill. The processor may parse the information to determine a tendency position for each legislator. The processor may transmit for display to a system user a virtual whipboard that groups legislators into a plurality of groups based on determined tendency positions. The processor may receive a selected one of the plurality of groups of legislators for a communication interaction based on the determined tendency position of the group and access a legislator database that includes legislative communication addresses of legislative personnel scraped from the Internet and divided into a plurality of legislative function categories and receive from the system user a selection of at least one of the plurality of legislative function categories.

Abstract: A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

Abstract: A prediction system provided with an integrated communications interface may include at least one processor configured to scrape the Internet to identify a currently pending legislative bill and information about legislators slated to vote on the pending bill. The processor may parse the information to determine a tendency position for each legislator. The processor may transmit for display to a system user a virtual whipboard that groups legislators into a plurality of groups based on determined tendency positions. The processor may receive a selected one of the plurality of groups of legislators for a communication interaction based on the determined tendency position of the group and access a legislator database that includes legislative communication addresses of legislative personnel scraped from the Internet and divided into a plurality of legislative function categories and receive from the system user a selection of at least one of the plurality of legislative function categories.

Abstract: A text analytics system may ascertain sentiment about multi-sectioned documents and may associate the sentiment with particular sections. The system may include at least one processor configured to scrape the Internet for text data associated with comments expressed by a plurality of individuals about a common multi-sectioned document. The comments may not be not linked to a particular section. The at least one processor may be further configured to analyze the text data in order to determine a sentiment associated with each comment; apply an association analysis filter to the text data in order to correlate at least a portion of each comment with one or more sections of the multi-sectioned document; and transmit for display to the system user a visualization of the sentiment mapped to one or more sections of the multi-sectioned document.

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.