Methods, systems, and media for presenting comments based on correlation with content

• US 9,401,947 B1
• Filed: 02/08/2013
• Issued: 07/26/2016
• Est. Priority Date: 02/08/2013
• Status: Expired due to Fees

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Oztaskent, Ant, Volovich, Yaroslav
• Primary Examiner(s): Kerzhner, Aleksandr
• Assistant Examiner(s): Levitian, Karina
• Application Number:
  US13/762,535
• Time in Patent Office: 1,264 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06F 16/686 using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings
• CPC Class Codes: G06F 16/70 of video data
• CPC Class Codes: G06F 16/78 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/7867 using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/226 Delivery according to priorities
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Methods, systems, and media for presenting comments based on correlation with content are provided. In some implementations, a method for presenting ranked comments is provided, the method comprising: receiving, using a hardware processor, content data related to an item of content; receiving, using the hardware processor, comment data related to a comment associated with the item of content; determining, using the hardware processor, a degree of correlation between at least a portion of the comment data and one or more portions of the content data; determining, using the hardware processor, a priority for the comment based on the degree of correlation; and presenting, using the hardware processor, the comment based on the priority.

Claims

1. A method for presenting ranked comments based on a correlation between a portion of a video and comments associated with the video, the method comprising:

receiving, using one or more hardware processors, first text data of words in a video being presented to a first user in a first instance of a user interface;

receiving second text data that is part of a first user comment posted by the first user in association with the video, wherein the first user comment was posted using the first instance of the user interface;

identifying a first string of characters from the second text data that is likely to include a quote of the first text data, wherein the first string of characters includes a plurality of words;

in response to identifying the first string of characters, comparing the first string of characters to at least a second string of characters from the first text data;

determining how different the first string of characters is from the second string of characters based on the comparison;

determining a degree of correlation between the first string of characters and the first text data based at least in part on how different the second string of characters is from the first string of characters, wherein the degree of correlation is less than it would be if there were no differences between the two strings by an amount that is based on how different the two strings of characters are;

generating a score for the first comment based at least in part on how closely the first string of characters quotes the second string of characters represented by the degree of correlation and based at least in part on a length of the first string of characters;

determining a ranking of a plurality of comments associated with the video, including the first user comment and a plurality of previously posted comments, based at least in part on the score;

causing at least two of the plurality of comments associated with the video to be presented to a second user in association with the video being presented in a second instance of the user interface in an order that is based at least in part on the ranking; and

causing a link to a portion of the video that corresponds to the second string of characters to be associated with the first user comment based on the degree of correlation between the first string of characters and the second string of characters, wherein selection of the link causes the portion of the video to be presented in the second instance of the user interface.

2. The method of claim 1, wherein the score is boosted based on the length of the first string of characters.

3. The method of claim 2, wherein determining the ranking further comprises comparing the score for the first user comment with a score for the plurality of previously posted comments.

4. The method of claim 3, wherein causing the comment to be presented to the second user comprises causing a first subset of the plurality of comments associated with the video having the highest scores to be presented in a first portion of a comment section of the second user interface and causing a second subset of the plurality of comments associated with the video that were posted most recently to be presented in a second portion of the comment section.

5. The method of claim 1, wherein the first text data comprises at least one of the following:

transcript data of words of dialogue in the video;

closed captioning data of words in the video;

optical character recognition generated from words in the video.

6. The method of claim 1, further comprising identifying a relevant portion of the second text data based on the determined degree of correlation between the at least a portion of the second text data and a portion of the first text data being greater than a threshold degree of correlation.

7. The method of claim 6, further comprising:

determining a degree of correlation between the relevant portion of the second text data and a relevant portion of third text data of a second user comment of the plurality of comments associated with the video; and

generating an aggregate comment by associating the first user comment and the second user comment upon determining that the degree of correlation between the relevant portions is over a second threshold degree of correlation.

8. The method of claim 7, further comprising generating an aggregate score based on the scores of the first user comment and the second user comment in response to aggregating the first user comment and the second user comment, and

wherein causing the first user comment to be presented based on the ranking further comprises causing the first user comment to be presented based on the aggregate score for the aggregated comment.

9. The method of claim 1, wherein comparing the first string of characters to at least the second string of characters from the first text data comprises calculating a distance metric between the two strings that indicates how different the two strings of text are.

10. The method of claim 9, wherein calculating the distance metric comprises calculating a hemming distance between the first string of characters and the second string of characters, wherein the first string of characters and the second string of characters are of equal length.

11. A system for presenting ranked comments based on a correlation between a portion of a video and comments associated with the video, the system comprising:

one or more hardware processors configured to;

receive first text data of words in a video presented to a first user in a first instance of a user interface;

receive second text data that is part of a first user comment posted by the first user in association with the video, wherein the first user comment was posted using the first instance of the user interface;

identify a first string of characters from the second text data that is likely to include a quote of the first text data, wherein the first string of characters includes a plurality of words;

in response to identifying the first string of characters, compare the first string of characters to at least a second string of characters from the first text data;

determine how different the first string of characters is from the second string of characters based on the comparison;

determine a degree of correlation between the first string of characters and the first text data based at least in part on how different the second string of characters is from the first string of characters, wherein the degree of correlation is less than it would be if there were no differences between the two strings by an amount that is based on how different the two strings of characters are;

generate a score for the first comment based at least in part on how closely the first string of characters quotes the second string of characters represented by the degree of correlation and based at least in part on a length of the first string of characters;

determine a ranking of a plurality of comments associated with the video, including the first user comment and a plurality of previously posted comments, based at least in part on the score;

cause at least two of the plurality of comments associated with the video to be presented to a second user in association with the video presented in a second instance of the user interface in an order that is based at least in part on the ranking; and

cause a link to a portion of the video that corresponds to the second string of characters to be associated with the first user comment based on the degree of correlation between the first string of characters and the second string of characters, wherein selection of the link causes the portion of the video to be presented in the second instance of the user interface.

12. The system of claim 11, wherein the score is boosted based on the length of the first string of characters.

13. The system of claim 12, wherein the one or more hardware processors are further configured to compare the score for the first user comment with a score for the plurality of previously posted comments.

14. The system of claim 13, wherein the one or more hardware processors are further configured to cause a first subset of the plurality of comments associated with the video having the highest scores to be presented in a first portion of a comment section of the second user interface and cause a second subset of the plurality of comments associated with the video that were posted most recently to be presented in a second portion of the comment section.

15. The system of claim 11, wherein the first text data comprises at least one of the following:

transcript data of words in the video;

closed captioning data of words in the video;

optical character recognition data generated from words in the video.

16. The system of claim 11, wherein the one or more hardware processors are further configured to identify a relevant portion of the second text data based on the determined degree of correlation between the at least a portion of the second text data and a portion of the first text data being greater than a threshold degree of correlation.

17. The system of claim 16, wherein the one or more hardware processors are further configured to:

determine a degree of correlation between the relevant portion of the second text data and a relevant portion of third text data of a second user comment of the plurality of comments associated with the video; and

generate an aggregate comment by associating the first user comment and the second user comment upon determining that the degree of correlation between the relevant portions is over a second threshold degree of correlation.

18. The system of claim 17, wherein the one or more hardware processors are further configured to:

generate an aggregate score based on the scores of the first user comment and the second user comment in response to aggregating the first user comment and the second user comment; and

causing the first user comment to be presented based on the aggregate score for the aggregated comment.

23. The non-transitory computer readable medium of claim 17, wherein the first text data comprises at least one of the following:

transcript data of words in the video;

closed captioning data of words in the video;

optical character recognition data generated from words in the video.

19. A non-transitory computer readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for presenting ranked comments based on a correlation between a portion of a video and comments associated with the video, the method comprising:

receiving first text data related to a video presented to a first user in a first instance of a user interface;

receiving second text data that is part of a first user comment posted by a first user in association with the video, wherein the first user comment was posted using the first instance of the user interface;

identifying a first string of characters from the second text data that is likely to include a quote of the first text data, wherein the first string of characters includes a plurality of words;

in response to identifying the first string of characters, comparing the first string of characters to at least a second string of characters from the first text data;

determining how different the first string of characters is from the second string of characters based on the comparison;

determining a degree of correlation between the first string of characters and the first text data based at least in part on how different the second string of characters is from the first string of characters, wherein the degree of correlation is less than it would be if there were no differences between the two strings by an amount that is based on how different the two strings of characters are;

generating a score for the first comment based at least in part on how closely the first string of characters quotes the second string of characters represented by the degree of correlation and based at least in part on a length of the first string of characters;

determining a ranking of a plurality of comments associated with the video, including the first user comment and a plurality of previously posted comments, based at least in part on the score;

causing at least two of the plurality of comments to be presented to a second user in association with the video presented in a second instance of the user interface in an order that is based at least in part on the ranking; and

causing a link to a portion of the video that corresponds to the second string of characters to be associated with the first user comment based on the degree of correlation between the first string of characters and the second string of characters, wherein selection of the link causes the portion of the video to be presented in the second instance of the user interface.

20. The non-transitory computer readable medium of claim 19, wherein the score is boosted based on the length of the first string of characters.

21. The non-transitory computer readable medium of claim 20, wherein determining the ranking further comprises comparing the score for the first user comment with a score for the plurality of previously posted comments.

22. The non-transitory computer readable medium of claim 21, wherein causing the comment to be presented to the second user comprises causing a first subset of the plurality of comments associated with the video having the highest scores to be presented in a first portion of a comment section of the second user interface and causing a second subset of the plurality of comments associated with the video that were posted most recently to be presented in a second portion of the comment section.

24. The non-transitory computer readable medium of claim 19, wherein the method further comprises identifying a relevant portion of the second text data based on the determined degree of correlation between the at least a portion of the second text data and a portion of the first text data being greater than a threshold degree of correlation.

25. The non-transitory computer readable medium of claim 24, wherein the method further comprises:

determining a degree of correlation between the relevant portion of the second data and a relevant portion of third text data of a second user comment; and

generating an aggregate comment by associating the first user comment and the second user comment upon determining that the degree of correlation between the relevant portions is over a second threshold degree of correlation.

26. The non-transitory computer readable medium of claim 25, wherein the method further comprises generating an aggregate score based on the scores of first user comment and the second user comment in response to aggregating the comment and second comment, and

wherein causing the comment to be presented based on the ranking further comprises causing the first comment to be presented based on the aggregate score for the aggregated comment.