Mentions-modules on online social networks

• US 10,270,882 B2
• Filed: 02/03/2016
• Issued: 04/23/2019
• Est. Priority Date: 02/03/2016
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Kazi, Rousseau Newaz, Rich, Mark Andrew, Sauper, Christina Joan, Herda{hacek over (g)}delen, Amac, Tanikella, Soorya Vamsi Mohan, Westervelt, Brett Matthew, Loomans, Maykel Andreas Louisa Jozef Anna, Bussing, Adam Eugene, Zheng, Shuyi
• Primary Examiner(s): Ho, Binh V
• Application Number:
  US15/014,911
• Publication Number:
  US 20170220579A1
• Time in Patent Office: 1,175 Days
• Field of Search: 707730
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: H04L 51/18 Commands or executable codes
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/53 using third party service providers

Abstract

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.

Claims

1. A method comprising, by one or more computing devices:

accessing, by the one or more computing devices, a plurality of communications authored by one or more users of an online social network, each communication being associated with a particular content item and comprising a text of the communication;

extracting, for each of the plurality of communications, one or more n-grams from the text of the communication;

identifying, by the one or more computing devices, one or more mention-terms from the one or more extracted n-grams, each mention-term being a noun-phrase;

calculating, by the one or more computing devices, a term-score for each mention-term based on a frequency of occurrence of the mention-term in the plurality of communications; and

generating, by the one or more computing devices, a mentions-module for a first user comprising one or more mentions, each mention comprising a mention-term having a term-score greater than a threshold term-score and text from one or more communications comprising the mention-term, wherein the mention-term is extracted from one or more representative communications from the plurality of communications, the representative communications being selected based on an affinity coefficient between the first user and an author of the representative communication.

2. The method of claim 1, further comprising:

accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising;

a first node corresponding to the first user, the first user being associated with the online social network; and

a plurality of second nodes corresponding to a plurality of communications of the online social network.

3. The method of claim 1, wherein the each communication is associated with the particular content item if the each communication includes a link to an instance of the particular content item.

4. The method of claim 1, wherein the identification of the one or more mention-terms from the one or more extracted n-grams comprises:

determining one or more noun phrases from the extracted one or more n-grams based on a part-of-speech analysis; and

filtering out one or more of the noun-phrases based on a level of relevance of the noun-phrases with respect to the particular content item as determined by a term frequency-inverse document frequency (TF-IDF) algorithm.

5. The method of claim 4, wherein the determination of the level of relevance by the TF-IDF algorithm is based on a frequency of usage of the one or more n-grams in communications associated with other content items related to a topic shared in common with the particular content item.

6. The method of claim 1, wherein the term-score of a mention-term is further based on one or more social signals associated with one or more of the communications comprising the mention-term.

7. The method of claim 1, wherein the term-score of a mention-term is further based on demographic information associated with one or more authors of one or more of the communications comprising the mention-term.

8. The method of claim 1, wherein the term-score of a mention-term is further based on whether the mention-term is included in one or more other search-results modules generated for the particular content item.

9. The method of claim 1, further comprising sending, to the client system of the first user, the mentions-module for display to the first user, wherein the first user is a user of the online social network.

10. The method of claim 1, wherein the term-score of a mention-term is further based on a location associated with the client system of the first user.

11. The method of claim 9, wherein each of the one or more representative communications has a representative-communication-score greater than a threshold representative-communication-score, the representative-communication-score for the representative communication being based on the affinity coefficient between the first user and the author of the representative communication.

12. The method of claim 1, wherein the text extracted from a representative communication comprises one or more n-grams preceding or following the one or more mention-terms within the representative communication.

13. The method of claim 11, wherein the representative-communication-score is further based on demographic information associated with the first user.

14. The method of claim 11 wherein the representative-communication-score is further based on a language associated with the first user.

15. The method of claim 11, wherein the representative-communication-score is further based on a user preference associated with the first user.

16. The method of claim 11, wherein the representative-communication-score is further based on a respective text quality of the representative communication.

17. The method of claim 1, wherein the mentions-module is generated for the particular content item if there are a threshold number of communications associated with the particular content item.

18. The method of claim 1, wherein the one or more mentions are purged from the one or more computing devices after a pre-defined period of time if its respective frequency of occurrence of the mention-terms included in the mentions is less than a minimum value within the plurality of communications.

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

access a plurality of communications authored by one or more users of an online social network, each communication being associated with a particular content item and comprising a text of the communication;

extract, for each of the plurality of communications, one or more n-grams from the text of the communication;

identify one or more mention-terms from the one or more extracted n-grams, each mention-term being a noun-phrase;

calculate a term-score for each mention-term based on a frequency of occurrence of the mention-term in the plurality of communications; and

generate a mentions-module for a first user comprising one or more mentions, each mention comprising a mention-term having a term-score greater than a threshold term-score and text from one or more communications comprising the mention-term, wherein the mention-term is extracted from one or more representative communications from the plurality of communications, the representative communications being selected based on an affinity coefficient between the first user and an author of the representative communication.

20. 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;

access a plurality of communications authored by one or more users of an online social network, each communication being associated with a particular content item and comprising a text of the communication;

extract, for each of the plurality of communications, one or more n-grams from the text of the communication;

identify one or more mention-terms from the one or more extracted n-grams, each mention-term being a noun-phrase;

calculate a term-score for each mention-term based on a frequency of occurrence of the mention-term in the plurality of communications; and

generate a mentions-module for a first user comprising one or more mentions, each mention comprising a mention-term having a term-score greater than a threshold term-score and text from one or more communications comprising the mention-term, wherein the mention-term is extracted from one or more representative communications from the plurality of communications, the representative communications being selected based on an affinity coefficient between the first user and an author of the representative communication.