14. The computing device of claim 11 , wherein identifying a set of sub clusters of the identified cluster comprises identifying the set of sub clusters according to any one or more of: a threshold volume of social communications within the sub cluster, a threshold percentage of the volume of social communications of the cluster within the sub cluster, or a threshold number of sub-clusters that have the greatest volume of social communications within the sub cluster.
11. A computing device configured to generate a social sketch corresponding to a time period, the computing device comprising a processor and a memory, and further comprises additional components for generating the social sketch, the additional components comprising: a social sketch generator configured to generate a social sketch from social communications obtained from one or more social networking services; a clustering module configured to cluster a corpus of social communications according to similarity; and a social sketch data store; wherein, in operation, the social sketch generator: obtains a corpus of social communications from one or more social networking services over a network via a network communication component, the corpus of social communications including social communications generated during a first time period; filters the obtained corpus of social communications according to the first time period such that the filtered social communications correspond to the social communications of the corpus of social communications generated during the first time period; clusters, by way of the clustering module, the filtered social communications according to the subject matter of the social communications to generate a plurality of clusters of filtered social communications; identifies a set of clusters of the plurality of clusters of social communications comprising identifying those clusters of the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster, wherein the plurality of clusters of social communications that have a sufficient volume of social communications within the cluster comprises a predetermined number of clusters that have the greatest number of social communications of the plurality of clusters, each cluster of the set of the clusters being an identified cluster; for each identified cluster: extracts a topic from the identified cluster according to the subject matter of the social communications of the identified cluster; identifies a representative image of the identified cluster from the social communications of the identified cluster; identifies a non-expert set of high-quality communications from the identified cluster, the non-expert set of high-quality communications corresponding to social communications of non-experts on the topic of the identified cluster; identifies an expert set of high-quality communications from the identified cluster, the expert set of high-quality communications corresponding to social communications of experts on the topic of the identified cluster; re-clusters, via the clustering module, the identified cluster of social communications; identifies a set of sub-clusters of the identified cluster; and extracts a sub-topic from each of the identified sub-clusters of the set of sub-clusters of the identified cluster; wherein the topic, the non-expert set of high-quality communications, the expert set of high-quality communications, the extracted sub-topics, and the representative image comprise a cluster set of the identified cluster; and stores the cluster sets of each of the identified clusters as the social sketch corresponding to the identified time period in the social sketch data store.