Systems, methods, and non-transitory computer readable media configured to determine scores for content items published in an online environment based on at least one machine learning model trained with features associated with the content items. The scores can be associated with probabilities that the content items include objectionable material. A subset of the content items can be selected based on scores of the subset of the content items and satisfaction of a threshold value. It can be determined whether the subset of the content items includes objectionable material.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: determining, by a computing system, scores for content items published in an online environment based on at least one machine learning model trained with features associated with the content items, the scores associated with probabilities that the content items include objectionable material; selecting, by the computing system, a subset of the content items based on scores of the subset of the content items and satisfaction of a threshold value; and determining, by the computing system, whether the subset of the content items includes objectionable material. 2. The computer-implemented method of claim 1, wherein the features reflect contextual information regarding the content items. 3. The computer-implemented method of claim 2, wherein the features relate to at least one of a user who flagged a content item and a user who uploaded a flagged content item. 4. The computer-implemented method of claim 3, wherein the features include at least one of reporting accuracy, abuse history, gender, age, profile completeness, profile verification, locale, friends counts, account age, number of reporters, language, and topics reflected by the content items. 5. The computer-implemented method of claim 1, wherein the content items include flagged content items. 6. The computer-implemented method of claim 1, wherein the at least one machine learning model is based on a random forest technique. 7. The computer-implemented method of claim 1, wherein the at least one machine learning model includes different machine learning models, the method further comprising developing the different machine learning models to identify objectionable material in different types of content items. 8. The computer-implemented method of claim 1, further comprising sorting the content items based on the scores. 9. The computer-implemented method of claim 1, wherein the determining whether the subset of the content items includes objectionable material comprises: presenting, via a computer enabled user interface, the subset of the content items for manual review; and receiving labels regarding whether the subset of the content items includes objectionable material based on the manual review. 10. The computer-implemented method of claim 9, further comprising retraining the at least one machine learning model based on the labels. 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: determining scores for content items published in an online environment based on at least one machine learning model trained with features associated with the content items, the scores associated with probabilities that the content items include objectionable material; selecting a subset of the content items based on scores of the subset of the content items and satisfaction of a threshold value; and determining whether the subset of the content items includes objectionable material. 12. The system method of claim 11, wherein the features reflect contextual information regarding the content items. 13. The system method of claim 12, wherein the features relate to at least one of a user who flagged a content item and a user who uploaded a flagged content item. 14. The system method of claim 13, wherein the features include at least one of reporting accuracy, abuse history, gender, age, profile completeness, profile verification, locale, friends counts, account age, number of reporters, language, and topics reflected by the content items. 15. The system method of claim 11, wherein the content items include flagged content items. 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: determining scores for content items published in an online environment based on at least one machine learning model trained with features associated with the content items, the scores associated with probabilities that the content items include objectionable material; selecting a subset of the content items based on scores of the subset of the content items and satisfaction of a threshold value; and determining whether the subset of the content items includes objectionable material. 17. The non-transitory computer-readable storage medium of claim 16, wherein the features reflect contextual information regarding the content items. 18. The non-transitory computer-readable storage medium of claim 17, wherein the features relate to at least one of a user who flagged a content item and a user who uploaded a flagged content item. 19. The non-transitory computer-readable storage medium of claim 18, wherein the features include at least one of reporting accuracy, abuse history, gender, age, profile completeness, profile verification, locale, friends counts, account age, number of reporters, language, and topics reflected by the content items. 20. The non-transitory computer-readable storage medium of claim 16, wherein the content items include flagged content items.