Systems and methods for predicting meme virality based on network structure

Patent metadata

Field	Value
Publication number	US-2016042284-A1
Application number	US-201414775403-A
Country	US
Kind code	A1
Filing date	Mar 12, 2014
Priority date	Mar 14, 2013
Publication date	Feb 11, 2016
Grant date	—

Abstract

Systems and methods for predicting virality of a content item are disclosed. A method includes: receiving a social network structure; identifying communities within the social network structure, where communities are identified as dense subnetworks in the social network structure; receiving social network content that includes one or more content items; and identifying one or more content items that are predicted to become viral based on utilization of the content items between different communities in the social network structure.

First claim

1 . A method for predicting virality of a content item, the method comprising:
receiving a social network structure; identifying communities within the social network structure, wherein communities are identified as dense subnetworks in the social network structure; receiving social network content that includes one or more content items; and identifying one or more content items that are predicted to become viral based on utilization of the content items between different communities in the social network structure.

2 . The method according to claim 1 , further comprising analyzing the social network content to identify new content items, wherein a content item is a new content item if there are less than a threshold number of instances of the content item in the social network content.

3 . The method according to claim 1 , wherein the communities identified as dense subnetworks have a larger proportion of connections between nodes within the community than connections to nodes outside the community.

4 . The method according to claim 1 , wherein the one or more content items comprise one or more of images, videos, webpages, metadata including Twitter hashtags, phrases, sentences, and names of people.

5 . The method according to claim 1 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that have been exposed to the content item within a threshold amount of time.

6 . The method according to claim 1 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that are connected to nodes that have been exposed to the content item within a threshold amount of time.

7 . The method according to claim 1 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that are connected within two degrees of separation to nodes that have been exposed to the content item within a threshold amount of time.

8 . The method according to claim 1 , wherein identifying one or more content items that are predicted to become viral is based on a maximum distance between any two nodes that have been exposed to the content item within a threshold amount of time.

9 . The method according to claim 1 , wherein identifying one or more content items that are predicted to become viral is based on a number of distinct communities that have used the content item.

10 . A computer-readable storage medium storing instructions that, when executed by a processor, cause a computer system to predict virality of a content item, by performing the steps of:
receiving a social network structure; identifying communities within the social network structure, wherein communities are identified as dense subnetworks in the social network structure; receiving social network content that includes one or more content items; and identifying one or more content items that are predicted to become viral based on utilization of the content items between different communities in the social network structure.

11 . The computer-readable storage medium according to claim 10 , further comprising analyzing the social network content to identify new content items, wherein a content item is a new content item if there are less than a threshold number of instances of the content item in the social network content.

12 . The computer-readable storage medium according to claim 10 , wherein the communities identified as dense subnetworks have a larger proportion of connections between nodes within the community than connections to nodes outside the community.

13 . The computer-readable storage medium according to claim 10 , wherein the one or more content items comprise one or more of images, videos, webpages, metadata including Twitter hashtags, phrases, sentences, and names of people.

14 . The computer-readable storage medium according to claim 10 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that have been exposed to the content item within a threshold amount of time.

15 . The computer-readable storage medium according to claim 10 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that are connected to nodes that have been exposed to the content item within a threshold amount of time.

16 . The computer-readable storage medium according to claim 10 , wherein identifying one or more content items that are predicted to become viral is based on a number of nodes in the social network structure that are connected within two degrees of separation to nodes that have been exposed to the content item within a threshold amount of time.

17 . The computer-readable storage medium according to claim 10 , wherein identifying one or more content items that are predicted to become viral is based on a maximum distance between any two nodes that have been exposed to the content item within a threshold amount of time.

18 . The computer-readable storage medium according to claim 10 , wherein identifying one or more content items that are predicted to become viral is based on a number of distinct communities that have used the content item.

19 . A computing system, comprising:
a processor; and a memory storing instructions that, when executed by the processor, cause the computing system to:
receive a social network structure;
identify communities within the social network structure, wherein the communities are identified as dense subnetworks in the social network structure;
receive social network content that includes one or more content items; and
identify one or more content items that are predicted to become viral based on utilization of the content items between different communities in the social network structure.

20 . The computing system according to claim 19 , wherein the social network structure and the social network content are received over a network.

Assignees

• Univ Indiana Res & Tech Corp

Inventors

• Menczer Filippo
• Weng Lilian
• Ahn Jr Yong Yeol

Classifications

• G06Q10/40

  Business processes related to social networking or social networking services · CPC title

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06Q10/44

  Identification of trends within social networks, e.g. identification of trending topics · CPC title