Techniques are disclosed for automatically modeling and predicting moderator actions for online content. A model can be generated or updated based on the content received and the action or actions taken by the moderator in response to receiving the content. The model can be used to automatically predict which action, or combination of actions, are likely to be taken by the moderator when new content is received, and suggest those action(s) to the moderator. These suggestions can, among other things, simplify and speed up the decision-making process for the moderator.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving first electronic content from a user computing device and an input representing an action taken by a moderator with respect to the first electronic content, the moderator being tasked with monitoring the first electronic content and facilitating further disposition of the first electronic content via the action taken; extracting a feature from the first electronic content; and generating or updating a predictive model based on the feature and the input, the predictive model configured to model a dependence of the action on a presence of the feature. 2. The method of claim 1, further comprising: receiving second electronic content from at least one of the user computing device and a different user computing device; extracting the feature from the second electronic content; determining, based on the predictive model and the feature, a suggested action to be taken by the moderator with respect to the second electronic content; and presenting the suggested action to the moderator. 3. The method of claim 1, wherein the feature includes at least one of a content-based feature, a user-based feature and a time-based feature. 4. The method of claim 1, wherein the predictive model includes a machine learning discriminative model configured to model the dependence of the action on the feature. 5. The method of claim 4, wherein the machine learning discriminative model includes a support vector machine classifier. 6. The method of claim 1, wherein the input represents a plurality of actions taken by the moderator with respect to the first electronic content, and wherein the machine learning discriminative model is further configured to model a dependence of at least one of the actions taken on at least one other of the actions taken. 7. The method of claim 6, further comprising determining an additional suggested action to be taken by the moderator based on the suggested action and the predictive model. 8. A system comprising: a storage; and a processor operatively coupled to the storage and configured to execute instruction stored in the storage that when executed cause the processor to carry out a process comprising: receiving first electronic content from a user computing device and an input representing an action taken by a moderator with respect to the first electronic content, the moderator being tasked with monitoring the first electronic content and facilitating further disposition of the first electronic content via the action taken; extracting a feature from the first electronic content; and generating or updating a predictive model based on the feature and the input, the predictive model configured to model a dependence of the action on a presence of the feature. 9. The system of claim 8, wherein the process further comprises: receiving second electronic content from at least one of the user computing device and a different user computing device; extracting the feature from the second electronic content; determining, based on the predictive model and the feature, a suggested action to be taken by the moderator with respect to the second electronic content; and presenting the suggested action to the moderator. 10. The system of claim 8, wherein the feature includes at least one of a content-based feature, a user-based feature and a time-based feature. 11. The system of claim 8, wherein the predictive model includes a machine learning discriminative model configured to model the dependence of the action on the feature. 12. The system of claim 11, wherein the machine learning discriminative model includes a support vector machine classifier. 13. The system of claim 8, wherein the input represents a plurality of actions taken by the moderator with respect to the first electronic content, and wherein the machine learning discriminative model is further configured to model a dependence of at least one of the actions taken on at least one other of the actions taken. 14. The system of claim 13, wherein the process further comprises determining an additional suggested action to be taken by the moderator based on the suggested action and the predictive model. 15. A non-transient computer program product having instructions encoded thereon that when executed by one or more processors cause a process to be carried out, the process comprising: receiving first electronic content from a user computing device and an input representing an action taken by a moderator with respect to the first electronic content, the moderator being tasked with monitoring the first electronic content and facilitating further disposition of the first electronic content via the action taken; extracting a feature from the first electronic content; and generating or updating a predictive model based on the feature and the input, the predictive model configured to model a dependence of the action on a presence of the feature. 16. The computer program product of claim 15, wherein the process further comprises: receiving second electronic content from at least one of the user computing device and a different user computing device; extracting the feature from the second electronic content; determining, based on the predictive model and the feature, a suggested action to be taken by the moderator with respect to the second electronic content; and presenting the suggested action to the moderator. 17. The computer program product of claim 15, wherein the feature includes at least one of a content-based feature, a user-based feature and a time-based feature. 18. The computer program product of claim 15, wherein the predictive model includes a machine learning discriminative model configured to model a dependence of the action on the feature. 19. The computer program product of claim 18, wherein the machine learning discriminative model includes a support vector machine classifier. 20. The computer program product of claim 15, wherein the input represents a plurality of actions taken by the moderator with respect to the first electronic content, wherein the machine learning discriminative model is further configured to model a dependence of at least one of the actions taken on at least one other of the actions taken, and wherein the process further comprises determining an additional suggested action to be taken by the moderator based on the suggested action and the predictive model.