Tracking changes in user-generated textual content on social media computing platforms

Patent metadata

Field	Value
Publication number	US-2016239581-A1
Application number	US-201514625446-A
Country	US
Kind code	A1
Filing date	Feb 18, 2015
Priority date	Feb 18, 2015
Publication date	Aug 18, 2016
Grant date	—

Abstract

Social media posts related to a topic are analyzed over time by parsing the posts to identify terms and by statistically analyzing occurrences and co-occurrences of the terms in the posts to derive metrics. A relationship-based structure is updated over time based on the metrics. A relationship-based structure is updated over time based on the metrics. In an example, the relationship-based structure includes weighted nodes and edges. The nodes represent terms in the posts and the edges represent co-occurrences of the terms. The weights of the nodes depend on frequencies of the occurrences, while as the weights of the edges depend on frequencies of the co-occurrences. A trend in the social media posts is detected by identifying a change over time in the relationship-based data structure.

First claim

1 . In a network-based environment for analyzing social media data, a computer-implemented method performed on a computer for detecting a trend in social media posts on one or more social media platforms, the computer-implemented method comprising:
tracking occurrences of terms in the social media posts on the one or more social media platforms; tracking co-occurrences of the terms in the social media posts by identifying terms that occur together in same social media posts; updating a relationship-based data structure over time with metrics regarding the occurrences and co-occurrences, the relationship-based data structure comprising nodes and edges connecting the nodes, the nodes representing the terms, the edges representing the co-occurrences of the terms, the nodes and edges weighted based on the metrics, weights of the nodes representing frequencies of the occurrences of the terms, and weights of the edges representing frequencies of the co-occurrences; and detecting the trend in the social media posts on the one or more social media platforms by identifying a change over time in the relationship-based data structure.

2 . The computer-implemented method of claim 1 , wherein detecting the trend comprises detecting terms of interest that form a topic or a story, wherein the terms of interest are detected by:
selecting a set of the terms by comparing the weights of the nodes to a threshold; and selecting the terms of interest from the set of the terms by comparing weights of edges associated with the set of the terms to another threshold.

3 . The computer-implemented method of claim 1 , wherein detecting the trend comprises identifying that a story is trending based on an increase in the weights.

4 . The computer-implemented method of claim 1 , wherein detecting the trend comprises identifying that a story is losing interest of users of the one or more social media platforms based on the weights falling below a threshold.

5 . The computer-implemented method of claim 1 , wherein detecting the trend comprises identifying that a story includes a particular term of interest based on an increase to a weight of a node representing the additional term of interest.

6 . The computer-implemented method of claim 1 , wherein a weight of a node representing a term is computed based on a frequency of occurrence of the term in the social media posts, a number of social media posts containing the terms, and a relevance of the social media posts containing the term.

7 . The computer-implemented method of claim 1 , wherein a weight of an edge connecting two nodes corresponding to two terms is computed based on a frequency of the two terms occurring together in the social media posts and a relevance of the two nodes.

8 . The computer-implemented method of claim 1 , wherein detecting the trend comprises partitioning the relationship-based structure in a plurality of partitions, wherein nodes belonging to at least one partition represent trending terms of the story.

9 . The computer-implemented method of claim 1 , wherein detecting the trend comprises partitioning the relationship-based structure in a plurality of partitions by:
taking snapshots of the relationship-based structure at time intervals; updating the relationship-based structure based on additional social media posts accessed between the time intervals; computing frequencies of occurrences of the terms between the time intervals; generating, as a function of the time intervals, similarities between the terms based on the frequencies of occurrences; and assigning the nodes to the partitions based on the similarities between the terms.

10 . The computer-implemented method of claim 1 , wherein detecting the trend comprises partitioning the relationship-based structure in a plurality of partitions by:
taking snapshots of the relationship-based structure at time intervals; updating the relationship-based structure based on additional social media posts accessed between the time intervals, the updating comprising increasing or decreasing weights of the edges based on co-occurrences of corresponding terms; and assigning the nodes to the partitions based on the weights of the edges.

11 . A computing system for analyzing social media posts regarding a story on one or more social media platforms, the computing system comprising:
a processor; and a memory comprising computer-readable instructions that, when executed by the processor, cause the computing system to at least:
analyze occurrences of terms in the social media posts and co-occurrences of the terms in same social media posts to generate metrics associated with the terms over time;
update a graph data structure over time based on the metrics, the graph data structure comprising nodes representing the terms and edges connecting the nodes and representing co-occurrences of the terms, the nodes and edges being weighted based on the metrics, weights of the nodes indicating frequencies of the occurrences of the terms, and weights of the edges indicating frequencies of the co-occurrences;
select a set of the terms by comparing the weights of the nodes or the edges to a threshold, wherein the set of terms represents a set of terms of interest; and
identify a trend of the story based on changes over time to the weights of nodes and edges corresponding to the set of terms, wherein the story comprises the set of terms of interest.

12 . The computing system of claim 11 , wherein identifying the trend comprises detecting the changes to the weight at time intervals, wherein the time intervals are defined as a function of a volume of the plurality of social media posts.

13 . The computing system of claim 11 , wherein updates to the nodes and edges are tracked over time, wherein the trend of the story is identified based on the updates.

14 . The computing system of claim 11 , wherein updates to the weights of the nodes and edges are tracked over time, wherein a start, an end, and a relevance of the story are identified based on the updates.

15 . The computing system of claim 11 , wherein the social media posts comprises a plurality of posts received from the one or more social media platforms, wherein the updates are tracked by at least:
taking a snapshot of the graph data structure at a point in time; receiving a post from a social media platform after the point in time; analyzing occurrences and co-occurrences of terms of the post to determine changes to the weights of the nodes and edges; and identifying the start, the end, or the relevance of the story based on the changes to the weights.

16 . The computing system of claim 11 , wherein the computer-readable instructions, when executed by the processor, further cause the computing system to at least:
track changes to the nodes or the edges over time by at least updating the metrics of the terms based on an analysis of social media posts received over time; and detect whether an interest in the story is increasing or decreasing based on the changes over time.

17 . A computer-readable storage medium storing instructions for analyzing social media posts regarding a story on one or more social media platform, the instructions when executed on a computing device configure the computing device to perform operations comprising:
tracking occurrences of terms in the social media posts; tracking co-occurrences of the terms in the social media posts by identifying terms that occur together in same social media posts; updating a graph data structure over time based on metrics associated with the occurrences

Assignees

• Adobe Systems Inc

Inventors

• Jaidka Kokil
• Kumaraguru Ponnurangam
• Chhaya Niyati
• Rustagi Sajal
• Gupta Prakhar
• Kaushik R

Classifications

• G06F16/345 Primary

  Summarisation for human users · CPC title

• G06F17/30958 Primary

  Physics · mapped topic

• G06F17/30876

  Physics · mapped topic

• G06F17/30368

  Physics · mapped topic

• H04L51/52

  for supporting social networking services · CPC title