A method of predictive analysis, including the steps of (a) selecting a sequence of discrete information signals for a time interval; (b) identifying one or more creators of the a group the discrete information signals, the one or more creators comprising a first group of social media participants; (c) selecting a group of observers of the sequence of discrete information signals, the observers comprising a second group of social media participants; (d) selecting a sub-group of observers from the group of the observers, the sub-group comprising one or more individuals of the group of observers who have taken external actions subsequent to the time interval; and (e) determining the existence or absence of one or more dependencies between the among the first group and the second group of media participants.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-14. (canceled) 15. A method of predictive analysis, the method comprising: (a) selecting, via a processor, a sequence of discrete information signals for a time interval; (b) identifying, via the processor, based on the selecting the sequence, one or more creator identifiers associated with said sequence of discrete information signals, wherein said one or more creator identifiers comprises a first group of social media participant identifiers; (c) selecting, via the processor, based on the selecting the sequence, a group of observer identifiers associated with said sequence of discrete information signals, wherein said observer identifiers comprise a second group of social media participant identifiers; (d) selecting, via the processor, a sub-group of observer identifiers from said group of said observer identifiers, wherein said sub-group comprises one or more individual identifiers of said group of observer identifiers, wherein the one or more individual identifiers have taken an external action subsequent to said time interval, wherein the selecting comprises identifying, via the processor, from said first and second groups of social media participant identifiers, a social media participant identifier of interest, wherein said participant identifier of interest comprises a participant having a statistically significant effect on said external action; (e) determining, via the processor, an existence or an absence of one or more dependencies between said first group and said second group; and (f) storing, via the processor, a datum informative of the existence. 16. The method of claim 15, wherein the step (e) comprises determining, via the processor, the existence or the absence of the one or more dependencies between the sequence of discrete information signals traced to said first group of social media participant identifiers and said external action associated with said second group of media participant identifiers. 17. The method of claim 16, wherein the step (a) comprises identifying, via the processor, a topic of interest, wherein said sequence of discrete information signals concern said subject of interest. 18. The method of claim 17, further comprising: generating, via the processor, a predictive analysis data of a future external action based on the existence of said one or more dependencies between said first group and said second group of media participant identifiers as identified in the datum based on the storing. 19. The method of claim 18, further comprising: determining, via the processor, a probability distribution of external actions based on a relationship between (1) a reactive data and (2) a social activity and an observation datum informative of observing participant identifiers. 20. A method of predictive analysis, the method comprising (a) selecting, via a processor, a sequence of discrete information signals for a time interval based upon a predetermined topic of interest; (b) identifying, via the processor, based on the selecting the sequence, one or more creator identifiers associated with said sequence of discrete information signals, wherein said one or more creator identifiers comprises a first group of social media participant identifiers; (c) selecting, via the processor, based on the selecting the sequence, a group of observer identifiers associated with said sequence of discrete information signals, wherein said observer identifiers comprise a second group of social media participant identifiers; (d) selecting, via the processor, a sub-group of observer identifiers from said group of said observer identifiers, wherein said sub-group comprises one or more individual identifiers of said group of observer identifiers, wherein the one or more individual identifiers have taken an external action subsequent to said time interval; (e) determining, via the processor, an existence or an absence of one or more dependencies between said first group and said second group; (f) storing, via the processor, a datum informative of the existence; and (g) generating, via the processor, a predictive analysis data of a future external action based on the existence of said one or more dependencies between said first group and said second group of media participant identifiers as identified in the datum (h) selecting, via the processor, one or more individual identifiers from said group of observer identifiers of said sequence of discrete information signals; and (i) predicting, via the processor, the future external action associated with said one or more individual identifiers based upon a current observation of discrete information signals for said time interval concerning said predetermined topic of interest, wherein said current observation of discrete information signals is associated with said one or more individual identifiers, wherein said social media participant identifier of interest comprises a participant having a statistically significant effect on said external action. 21. The method of claim 20, wherein the step (e) comprises determining, via the processor, the existence or the absence of the one or more dependencies between the sequence of discrete information signals traced to said first group of social media participant identifiers and said external action associated with said second group of media participant identifiers. 22. The method of claim 21, wherein the step (d) comprises identifying, via the processor, from said first and second groups of social media participant identifiers, a social media participant identifier of interest. 23. The method of claim 22, further comprising: determining, via the processor, a probability distribution of external actions based on a relationship between (1) a reactive data and (2) a social activity and an observation datum informative of observing participant identifiers. 24. A method of predictive analysis, the method comprising: (a) selecting, via a processor, a sequence of discrete information signals for a time interval; (b) identifying, via the processor, based on the selecting the sequence, one or more creator identifiers associated with said sequence of discrete information signals, wherein said one or more creator identifiers comprises a first group of social media participant identifiers; (c) selecting, via the processor, based on the selecting the sequence, a group of observer identifiers associated with said sequence of discrete information signals, wherein said observer identifiers comprise a second group of social media participant identifiers; (d) selecting, via the processor, a sub-group of observer identifiers from said group of said observer identifiers, wherein said sub-group comprises one or more individual identifiers of said group of observer identifiers, wherein the one or more individual identifiers have taken an external action subsequent to said time interval, wherein the selecting comprises identifying, via the processor, from said first and second groups of social media participant identifiers, a social media participant identifier of interest, wherein said social media participant identifier of interest comprises a participant having a statistically significant effect on said external action; (e) determining, via the processor, an existence or an absence of one or more dependencies between said first group and said second group based on the existence or the absence of the one or more dependencies between the sequence of discrete information signals traced to said first group of social media participant identifiers and said external action associated with said second group of media participant identifiers; (f) storing, via the processor, a datum informative of the existence; (g) determining, via the processor, a probability distribution of external actions based on a relationship between (1) a reactive data and (2) a social activity and an observation datum informative of observing participant identifiers. 25. The method of claim 24, wherein the step (a) comprises identifying, via the processor, a topic of interest, wherein said sequence of discrete information signals concern said subject of interest. 26. The method of claim 25, further comprising: generating, via the processor, a predictive analysis data of a future external action based on the existence of said one or more dependencies between said first group and said second group of media participant identifiers as identified in the datum. 27. The method of claim 26, wherein the step (a) comprises selecting, via the processor, said sequence of discrete information signals for said time interval based upon a predetermined topic of interest. 28. The method of claim 27, further comprising: (h) selecting, via the processor, one or more individual identifiers from said group of observer identifiers of said sequence of discrete information signals; and (i) predicting, via the processor, the future external action associated with said one or more individual identifiers based upon a current observation of discrete information signals for said time interval concerning said predetermined topic of interest, wherein said current observation of discrete information signals is associated with said one or more individual identifiers.