In one embodiment, a method includes using a processing system to access a first set and a second set of user accounts in an online social network. The first set and second set of user accounts are predetermined as belonging to a first category and a second category, respectively. From each user account in the first and second set, the system may extract feature values corresponding to a set of predetermined feature types, which includes at least a feature type relating to profile information and at least a feature type relating to posting information. The system may then train a machine-learning model using the extracted feature values. The trained machine-learning model may be configured to predict whether a third user account in the online social network belongs to the first category or the second category, based feature values corresponding to the feature types extracted from the third user account.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: accessing, by a computer processing system, a first plurality of user accounts in an online social network, the first plurality of user accounts being predetermined as belonging to a first category; accessing, by the computer processing system, a second plurality of user accounts in the online social network, the second plurality of user accounts being predetermined as belonging to a second category; from each of the first plurality of user accounts and each of the second plurality of user accounts, extracting, by the computer processing system, feature values corresponding to a set of predetermined feature types, wherein the set of predetermined feature types comprises (1) at least a first feature type relating to profile information associated with the corresponding user account and (2) at least a second feature type relating to posting information associated with the corresponding user account; and training, by the computer processing system, a machine-learning model using the feature values extracted from the first plurality of user accounts and the second plurality of user accounts; wherein the trained machine-learning model is configured to predict whether a third user account in the online social network belongs to the first category or the second category based on one or more feature values extracted from the third user account, the one or more feature values corresponding to one or more of the predetermined feature types. 2. The method of claim 1, wherein the first feature type relates to biographical information. 3. The method of claim 1, wherein the first feature type relating to profile information is based on a word-length measure of the profile information. 4. The method of claim 1, wherein the first feature type relating to profile information is based on occurrences of predetermined words in the profile information. 5. The method of claim 4, wherein the first feature type relating to profile information is further based on an aggregation of coefficients associated with the predetermined words. 6. The method of claim 5, wherein the coefficients associated with the predetermined words are computed based on (1) occurrences of the predetermined words occurring in the profile information of the first plurality of user accounts and (2) occurrences of the predetermined words occurring in the profile information of the second plurality of user accounts. 7. The method of claim 1, wherein the first feature type relating to profile information is based on word vectors occurring in the profile information. 8. The method of claim 1, wherein the first feature type relating to profile information is based on paragraph vectors occurring in the profile information. 9. The method of claim 1, wherein the first feature type relating to profile information is based on whether a website link satisfying a predetermined format occurs in the profile information. 10. The method of claim 1, wherein the second feature type relating to posting information is based on occurrences of tagging metadata in the posting information. 11. The method of claim 1, wherein the second feature type relating to posting information is based on a frequency of a tagging metadata occurring in the posting information. 12. The method of claim 10, wherein the tagging metadata are associated with media posted on the online social network. 13. The method of claim 10, wherein the tagging metadata comprise hashtags. 14. The method of claim 1, wherein the second feature type relating to posting information is based on a determination of similarities between images included in the posting information. 15. The method of claim 14, wherein the determination of similarities between images comprises: extracting a feature vector from each of the images; and clustering the images based on distances between the feature vectors. 16. The method of claim 1, wherein the second feature type relating to posting information is based on a time at which the posting information is posted. 17. The method of claim 1, wherein the training comprises using regression analysis. 18. The method of claim 1, wherein the first category is user accounts being used for business purposes in the online social network, and wherein the second category is user accounts being used for non-business purposes in the online social network. 19. A system comprising: a processing system; and computer-readable memory in communication with the processing system encoded with instructions for commanding the processing system to execute steps comprising: accessing a first plurality of user accounts identified as belonging to a first category; accessing a second plurality of user accounts identified as belonging to a second category; from each of the first plurality of user accounts and each of the second plurality of user accounts, extracting feature values corresponding to a set of predetermined feature types, wherein the set of predetermined feature types comprises (1) at least a first feature type relating to profile information associated with the corresponding user account and (2) at least a second feature type relating to posting information associated with the corresponding user account; and training a machine-learning model using the feature values extracted from the first plurality of user accounts and the second plurality of user accounts; wherein the trained machine-learning model is configured to predict whether a third user account in the online social network belongs to the first category or the second category based on one or more feature values extracted from the third user account, the one or more feature values corresponding to one or more of the predetermined feature types. 20. A non-transitory computer-readable storage medium comprising computer executable instructions which, when executed, cause a processing system to execute steps comprising: accessing a first plurality of user accounts identified as belonging to a first category; accessing a second plurality of user accounts identified as belonging to a second category; from each of the first plurality of user accounts and each of the second plurality of user accounts, extracting feature values corresponding to a set of predetermined feature types, wherein the set of predetermined feature types comprises (1) at least a first feature type relating to profile information associated with the corresponding user account and (2) at least a second feature type relating to posting information associated with the corresponding user account; and training a machine-learning model using the feature values extracted from the first plurality of user accounts and the second plurality of user accounts; wherein the trained machine-learning model is configured to predict whether a third user account in the online social network belongs to the first category or the second category based on one or more feature values extracted from the third user account, the one or more feature values corresponding to one or more of the predetermined feature types.