Method and system for temporal correlation of social signals

• US 9,288,123 B1
• Filed: 12/07/2012
• Issued: 03/15/2016
• Est. Priority Date: 08/31/2012
• Status: Active Grant

• Current Assignee: TPG Specialty Lending Incorporated
• Original Assignee: Sprinklr Inc.
• Inventors: De Oliveira, John Joseph, Huddleston, Brian, Safford, Kevin, Hudleston, Erik Lee
• Primary Examiner(s): Nguyen, Tammy
• Application Number:
  US13/708,020
• Time in Patent Office: 1,194 Days
• Field of Search: 709/204, 709/203, 709/206, 707/673, 707/780, 707/738
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A social analytic system may collect social signals from different social network accounts. The social signals may be associated with different ecosystems. Time series data may be generated from the social signals and the time series data may be filtered to remove at least some generic or unrelated trends. Different data sets from the time series data may be associated with different ecosystem metrics. The social analytic system may compare different filtered time series data sets to identify different ecosystem events. For example, the comparisons may be used to identify highly correlated ecosystem metrics and ecosystem anomalies, and predict ecosystem events.

Claims

1. A method, comprising:

collecting occurrences of social signals associated with an ecosystem, wherein the social signals comprise content and metadata for messages sent or posted on social networks;

generating time series data identifying a number of the occurrences of the messages for different time periods;

filtering at least some generic or unrelated trends from the time series data by normalizing the number of occurrences of the messages for the different time periods;

identifying events in the ecosystem based on changes in the number of occurrences of the messages for the different time periods in the filtered time series data;

identifying a first data set from the filtered time series data comprising web interactions of users having a market relationship with a company web account, wherein the web interactions include generating and viewing messages in the company web account;

identifying a second data set from the filtered time series data comprising web interactions of users having an influencer relationship with the company web account, wherein the users having the influencer relationship have a larger number of followers or subscribers in the social networks than the users having the market relationship;

generating a correlation value between the first data set with the second data set;

identifying a change in the second data set generated by the users having the influencer relationship; and

predicting a change in the first data set generated by the users having the market relationship based on the change in the second data set and the correlation value between the first data set and the second data set.

2. The method of claim 1, wherein the generic or unrelated trends comprise linear changes in the number of occurrences of the messages.

3. The method of claim 1, wherein the generic or unrelated trends comprise periodic changes in the number of occurrences of the messages.

4. The method of claim 1, wherein the generic or unrelated trends comprise seasonal trends associated with social network patterns for different times of a day, week, month, and year.

5. The method of claim 1, wherein filtering at least some of the generic or unrelated trends from the time series data comprises applying differencing algorithms and linear regression algorithms to different data sets from the time series data associated with different social metrics.

6. The method of claim 1, wherein identifying the events comprises identifying anomalies in the filtered time series data.

7. The method of claim 6, wherein identifying the anomalies comprises:

identifying an ecosystem trend in the filtered time series data;

comparing values of the ecosystem trend to values of the filtered time series data at corresponding times; and

identifying the values of the filtered time series data outside of a range of the values of the ecosystem trend as the anomalies.

8. The method of claim 1, wherein identifying the events comprises:

identifying a rate of change in values for the filtered time series data;

identifying portions of the filtered time series data where the rate of change is outside a threshold rate.

9. An apparatus, comprising:

memory configured to store social signals comprising messages generated, sent, and viewed by users; and

a processor configured to;

collect the social signals associated with an ecosystem, wherein the ecosystem comprises the messages generated, sent, and viewed by the users on social media website accounts associated with a company;

identify different types of constituents generating the social signals;

generate time series data from the social signals;

generate correlation values between different data sets in the time series data associated with the different types of constituents;

identifying events related to the company based on the correlation values between the different data sets associated with the different types of constituents;

identify a first one of the data sets with the social signals generated by a first set of users having a first type of constituent user relationship with the company;

identify a second one of the data sets with the social signals generated by a second set of users having a second type of constituent user relationship with the company;

detect responses in the messages of the first set of users in the first one of the data sets; and

predict responses in the messages of the second set of users in the second one of the data sets based on the responses of the first set of users in the first one of the data sets and the correlation values generated between the first one of the data sets and the second one of the data sets.

10. The apparatus of claim 9, wherein the processor is further configured to:

identify sentiments of the constituents generating the social signals;

identify a number of the social signals generated by the different types of constituents;

identifying the events related to the company based on changes in the sentiments of the constituents, the number of social signals generated by the different types of constituents, and the correlation values between the different data sets associated with the different types of constituents.

11. The apparatus of claim 9, wherein:

the first set of users have advocate relationships with the company and generate overall positive messages associated with the company; and

the second set of users includes other users that generate or view messages in the company social media website accounts but do not have advocate relationships with the company.

12. The apparatus of claim 10, wherein the processor is further configured to:

identify an increase in the sentiments for the first one of the data sets; and

predict an increase in an overall number of messages generated by the second set of users based on the increase in sentiments for the first one of the data sets.

13. The apparatus of claim 9, wherein the processor is further configured to identify anomalies in the data sets.

14. The apparatus of claim 13, wherein the processor is further configured to

identify ecosystem trends in the data sets;

compare values of the ecosystem trends to values of the data sets at corresponding times; and

identify the values in the data sets outside of a range of the values of the ecosystem trends as the anomalies.

15. The apparatus of claim 9, wherein the processor is further configured to:

identify changes in values in the data sets; and

identify the changes above a threshold rate.

16. The apparatus of claim 9, wherein the processor is further configured to generate one of the correlation values between the first one of the data sets associated with a first ecosystem metric and the second one of the data sets associated with a second ecosystem metric.

17. The apparatus of claim 9, wherein the processor is further configured to:

detect a first one of the events in the first one of the data sets; and

predict a second one of the events in the second one of the data sets based on detection of the first one of the events and the generated one of the correlation values between the first one of the data sets and the second one of the data sets.

18. The apparatus of claim 9, wherein the processor is further configured to:

generate the first one of the data sets for an ecosystem metric, wherein the first one of the data sets provides a historic social signal pattern for the ecosystem metric;

generate the second one of the data sets for the ecosystem metric, wherein the second one of the data sets provides a current social signal pattern for the ecosystem metric;

compare values for the first one of the data sets with values for the second one of the data sets at corresponding time periods; and

identify the values for the second one of the data sets that is outside of a range of the values for the first one of the data sets.

19. A system, comprising:

memory configured to store social signals comprising messages generated, sent, and viewed by users on social media website accounts associated with a company;

a processing device configured to;

generate time series data sets from the social signals, wherein the data sets are associated with different metrics including a signal count identifying a number of messages generated, sent, and viewed over time;

filter at least some generic trends from the time series data sets;

identify a first one of the data sets with social signals generated by a first set of users having a first type of constituent user relationship with the company;

identify a second one of the data sets with social signals generated by a second set of users having a second type of constituent user relationship with the company;

calculate correlation values between the first one of the data sets and the second one of the data sets;

detect responses of the first set of users in the first one of the data sets; and

predict responses of the second set of users in the second one of the data sets based on the responses of the first set of users in the first one of the data sets and the correlation values generated between the first one of the data sets and the second one of the data sets.