Facebook, Inc.

Shuyi Zheng

Rousseau Newaz Kazi

Brett Matthew Westervelt

Amaç Herdagdelen

Christina Joan Sauper

Mark Andrew Rich

Maykel Andreas Louisa Jozef Anna Loomans

Soorya Vamsi Mohan Tanikella

Adam Eugene Bussing

Alexander Paul Mentch

In one embodiment, a method includes receiving, from a link-search interface of a client device, a search query from a first user of an online social network. The link-search interface is associated with a composer interface in which the first user is composing a first post. The method further includes searching a web index of an online social network to identify one or more external objects matching a search query. The web index identifies a plurality of external objects hosted by third-party systems that have been posted to the online social network, and each search result comprises a reference to the respective external object and a link to the external object. The method further includes embedding, in the first post, a link to a first external object referenced by a first search result selected by the first user.

Embedding links in user-created content 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

Fremont, CA, United States of America

Shuyi Zheng