Method and system for temporal correlation of social signals

摘要

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.

主权项

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.