Facebook, Inc.

Soorya Vamsi Mohan Tanikella

Rousseau Newaz Kazi

Amaç Herdagdelen

Brett Matthew Westervelt

Mark Andrew Rich

Christina Joan Sauper

Maykel Andreas Louisa Jozef Anna Loomans

Shuyi Zheng

Adam Eugene Bussing

Li-Tal Mashiach

Ivan Mazepov

Apurva Sinha

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.

Generating news headlines on online social networks

In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, n-grams from the text of the communication; identifying mention-terms from the extracted n-grams, each mention-term being a noun-phrase; calculating a term-score for each mention-term based on a frequency of occurrence of the mention-term in the communications; and generating a mentions-module including mentions, each mention including a mention-term having a term-score greater than a threshold term-score and text from communications comprising the mention-term.

Mentions-modules on online social networks

In one embodiment, a method includes receiving, from a client system of a first user, a request associated with a particular content item; identifying communications authored by one or more users, each identified communication being associated with the particular content item; generating one or more search-results modules related to the particular content item, each search-results module being of a particular module type, wherein each search-results module includes information from a subset of the identified communications, the information corresponding to the particular module type of the search-results module, and wherein a number of communications in the subset of the identified communications including each search-results module is greater than a module-specific threshold number of communications; and sending, to the client system, a search-results interface comprising one or more of the search-results modules.

Search-results interfaces for content-item-specific modules on online social networks

In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.

Sentiment-modules on online social networks

In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, quotations from the text of the communication; determining, for each extracted quotation, partitions of the quotation; grouping the extracted quotations into clusters based on a respective degree of similarity among their respective partitions; calculating a cluster-score for each cluster based on a frequency of occurrence of partitions of quotations in the cluster in the communications; and generating a quotations-module comprising representative quotations, each representative quotation being a quotation from a cluster having a cluster-score greater than a threshold cluster-score.

Growing community of inventors

Redmond, WA, United States of America

Soorya Vamsi Mohan Tanikella