Facebook, Inc.

Adam Eugene Bussing

Rousseau Newaz Kazi

Amaç Herdagdelen

Brett Matthew Westervelt

Mark Andrew Rich

Christina Joan Sauper

Maykel Andreas Louisa Jozef Anna Loomans

Shuyi Zheng

Soorya Vamsi Mohan Tanikella

Dirk John Stoop

Balmanohar Paluri

Oliver Scholz

In one embodiment, a method includes receiving a query from a user for videos; identifying videos matching the query; retrieving, for each identified video, a set of keyframes that are associated with one or more concepts; calculating, for each keyframe of each identified video, a keyframe-score based on a prevalence of the concepts associated with the keyframe, determined with reference to the concepts associated with each other keyframe in the set of retrieved keyframes for the identified video; and sending, to the first user, a search-results interface including search results corresponding to one or more of the identified videos, each search result comprising keyframes for the corresponding identified video having keyframe-scores greater than a threshold keyframe-score.

Video keyframes display 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

San Francisco, CA, United States of America

Adam Eugene Bussing