Method and system for temporal correlation of social signals
First Claim
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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.
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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.
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Citations
19 Claims
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1. A method, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus, comprising:
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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. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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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.
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Specification