Abstract: In one embodiment, a method includes identifying a trending topic on an online social network, accessing a plurality of content objects posted to the online social network, wherein each content object is associated with the trending topic, and categorizing each content object into clusters based on a natural-language analysis of the content objects. The method may further include calculating a quality score for each cluster, wherein the quality score for each cluster is based at least on a measure of recency of one or more publication dates of the content objects within the cluster, select the cluster with the highest quality score as a trending cluster, and generating a trending-topic interface that includes a headline and description of the trending topic, wherein the headline and description are extracted from one or more of the content objects within the trending cluster.

Abstract: In one embodiment, a method includes identifying a trending topic on an online social network, accessing a plurality of content objects posted to the online social network, wherein each content object is associated with the trending topic, and categorizing each content object into clusters based on a natural-language analysis of the content objects. The method may further include calculating a quality score for each cluster, wherein the quality score for each cluster is based at least on a measure of recency of one or more publication dates of the content objects within the cluster, select the cluster with the highest quality score as a trending cluster, and generating a trending-topic interface that includes a headline and description of the trending topic, wherein the headline and description are extracted from one or more of the content objects within the trending cluster.