Some embodiments include a method of performing a content analysis study around a central theme utilizing a concept study system. The concept study system can generate a classifier machine corresponding to the content analysis study based on a super topic taxonomy including one or more concept identifiers. The concept study system can process a content object, associated with a user activity in a social networking system, through the classifier machine to determine whether to assign the user activity to the content analysis study. The concept study system can aggregate at least an attribute derived from the user activity in a study-specific data container associated with the content analysis study and compute a statistical or analytical insight based on aggregated attributes in the study-specific data container.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: generating a finite automaton machine corresponding to a topical content analysis study based on a super topic taxonomy including one or more concept identifiers; processing a content object, associated with a user activity in a social networking system, through the finite automaton machine to determine whether to assign the user activity to the topical content analysis study; aggregating at least an attribute derived from the user activity in a study-specific data container associated with the topical content analysis study; and computing a statistical or analytical insight based on aggregated attributes in the study-specific data container, wherein the statistical or analytical insight includes a computer-rendered illustration or a computational measurement from processing the aggregated attributes. 2. The computer-implemented method of claim 1, wherein the super topic taxonomy includes a hashtag, a topic tag, a term object comprising two or more consecutive words, or any combination thereof. 3. The computer-implemented method of claim 1, wherein processing the content object is in response to the social networking system receiving the user activity from a user device. 4. The computer-implemented method of claim 1, wherein processing the content object is asynchronous from the social networking system receiving the user activity. 5. The computer-implemented method of claim 1, wherein computing the statistical or analytical insight is in response to processing the content object; and wherein computing the statistical or analytical insight includes updating the statistical or analytical insight on a user interface in real-time or substantially real-time based on inclusion of the attribute derived from the user activity in the study-specific data container. 6. The computer-implemented method of claim 1, further comprising: extracting a user identifier from the user activity; and deriving a user demographic as the attribute for aggregation by accessing a user profile corresponding to the user identifier from the social networking system. 7. The computer-implemented method of claim 1, wherein the attribute is a user identifier of an acting user of the user activity; and wherein the study-specific data container is an audience segmentation corresponding to the topical content analysis study. 8. The computer-implemented method of claim 7, wherein computing the statistical or analytical insight is based on demographic information of user identifiers in the audience segmentation. 9. The computer-implemented method of claim 1, further comprising extracting geolocation information from the user activity as the attribute for aggregation. 10. The computer-implemented method of claim 1, wherein processing the content object includes determining that a concept identifier in the super topic taxonomy is in or associated with the content object; and wherein aggregating at least the attribute includes increasing a tally of one or more content objects that have or are associated with the concept identifier. 11. The computer-implemented method of claim 1, wherein processing the content object includes determining that a concept identifier in the super topic taxonomy of a particular concept identifier type is in or associated with the content object; and wherein aggregating at least the attribute includes increasing a tally of one or more content objects that have or are associated with a concept identifier of the particular concept identifier type. 12. The computer-implemented method of claim 1, wherein processing the content object includes: determining that the user activity corresponds to a content engagement activity to engage the content object; and providing the content object as input to the finite automaton machine. 13. The computer-implemented method of claim 1, wherein processing the content object includes: determining that the user activity corresponds to a content generation activity that produces the content object; and providing the content object as input to the finite automaton machine. 14. The computer-implemented method of claim 1, wherein computing the statistical or analytical insight is by comparing a statistical measure of the aggregated attributes in the study-specific data container to a baseline statistical measure of a superset of attributes. 15. The computer-implemented method of claim 1, further comprising expiring the finite automaton machine when a time threshold is met. 16. A computer readable data storage memory storing computer-executable instructions that, when executed by a computer system, cause the computer system to perform a computer-implemented method, the instructions comprising: instructions for generating a classifier machine corresponding to a content analysis study based on a super topic taxonomy associated with a central theme for a topical content analysis study; instructions for identifying a content object associated with a user activity in a social networking system; instructions for processing the content object through the classifier machine to determine whether to assign the user activity or the content object to the topical content analysis study; instructions for aggregating at least a user identifier derived from the user activity in an audience segmentation associated with the topical content analysis study; and instructions for computing a statistical or analytical insight based on demographic profile of the audience segmentation, wherein the statistical or analytical insight includes a computer-rendered illustration or a computational measurement from processing the aggregated attributes. 17. The computer readable data storage memory of claim 16, wherein the instructions further comprises: instructions for presenting another content object to one or more members of the audience segmentation to target the members that are interested in a central theme represented by the super topic taxonomy. 18. The computer readable data storage memory of claim 16, wherein the classifier machine includes a Boolean expression, a regular expression, a decision tree/trie, a dictionary, or any combination thereof. 19. The computer readable data storage memory of claim 16, wherein the instructions further comprises: instructions for training the classifier model utilizing supervised or unsupervised machine learning and labeled content. 20. A social networking system, comprising: a classifier machine repository storing one or more active classifier machines; a machine generator engine configured to generate a classifier machine corresponding to a topical content analysis study based on a super topic taxonomy having one or more concept identifiers and to store the classifier machine in the classifier machine repository; a study-specific data aggregation container associated with the topical content analysis study; and an activity processor configured to implement a machines aggregate combining the active classifier machines in the classifier machine repository to process a content object associated with a user activity and to aggregate at least an attribute of the content object or the user activity in the study-specific data container.