Generating news headlines on online social networks

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• identify a trending topic on an online social network;
• access a plurality of content objects posted to the online social network, wherein each content object is associated with the trending topic;
• categorize each content object into one of a plurality of discrete clusters based on a natural-language analysis of the respective content objects, wherein the natural-language analysis is a term frequency-inverse document frequency (TF-IDF) analysis, wherein the trending topic is associated with a plurality of sub-topics, and wherein each cluster relates to a different sub-topic of the plurality of sub-topics;
• calculate 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
• generate a trending-topic interface comprising a headline and description of the trending topic, wherein the headline and description are each generated based on one or more text strings extracted from one or more of the content objects within the trending cluster.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• identify a trending topic on an online social network;
• access a plurality of content objects posted to the online social network, wherein each content object is associated with the trending topic;
• categorize each content object into one of a plurality of discrete clusters based on a natural-language analysis of the respective content objects, wherein the natural-language analysis is a term frequency-inverse document frequency (TF-IDF) analysis, wherein the trending topic is associated with a plurality of sub-topics, and wherein each cluster relates to a different sub-topic of the plurality of sub-topics;
• calculate 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
• generate a trending-topic interface comprising a headline and description of the trending topic, wherein the headline and description are each generated based on one or more text strings extracted from one or more of the content objects within the trending cluster.