One or more processor to receives content over distributed network media sources and isolated content, authored by one or more users of the plurality of users, in which the content and isolated content are directed to a plurality of topics. The content and isolated content are parsed into content segments and aggregated based on topics to which the content segments are directed, and users authoring the content segments are identified, the aggregated content segments forming at least part of a discussion thread. One or more processors determine a probability that the discussion thread is complete, based on a set of user behavioral pattern metrics generated by analysis of historical content, and one or more processors, in response to the discussion thread being substantially complete and exceeding a probability threshold, determines the discussion thread, including the content and the isolated content to be ready for a contextual relationship analysis.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: one or more processors gathering content authored by a plurality of users over distributed network media sources, and isolated content authored by one or more users of the plurality of users, wherein the content and isolated content are directed to a plurality of topics; one or more processors parsing the content and the isolated content; one or more processors aggregating the content that is parsed and the isolated content that is parsed, which are directed to a topic of the plurality of topics, wherein the content that is parsed and the isolated content that is parsed, which are aggregated, form at least a portion of a discussion thread; one or more processors identifying a set of users of the plurality of users, authoring the content that is parsed and the isolated content that is parsed, which are directed to the topic; one or more processors determining a probability of whether the discussion thread is at least substantially complete, based on a set of metrics of behavioral patterns of the plurality of users, identified by analysis of historical content and isolated historical content authored by the plurality of users; and in response to determining the probability of the discussion thread to exceed a probability threshold indicating the discussion thread to be at least substantially complete, one or more processors performing a contextual relationship analysis between instances of the content that is parsed and the instances of the isolated content that is parsed, which are directed to the topic, and are authored by the set of users of the plurality of users. 2. The method of claim 1, further comprising: one or more processors gathering historical content authored by the plurality of users over distributed network sources, wherein the historical content includes historical social media content and historical isolated content, and wherein the historical content and historical isolated content includes a plurality of topics; one or more processors parsing the historical content and historical isolated content into a plurality of historical content segments, each historical content segment of the plurality of historical content segments associated with a topic of the plurality of topics and a user of the plurality of users; and one or more processors determining a set of behavioral metrics for each user of the plurality of users, based on activities of contribution with respect to each topic of the plurality of topics, on the historical content and historical isolated content, wherein a probability of a user adding additional content to a discussion thread is derived from the set of behavioral metrics for each user of the plurality of users. 3. The method of claim 2, further comprising: one or more processors creating a psycholinguistic profile of each user of the plurality of users, which includes personality attributes and behavioral traits by an analysis of words and phrases used in social media interactions of the historical content authored by the plurality of users; and one or more processors applying the psycholinguistic profile for each of the plurality of users to aggregated content segments of a discussion thread to determine, at least in part, a probability that the discussion thread is substantially complete. 4. The method of claim 1, further comprising: one or more processors tracking content authored by each user of the discussion thread to determine whether a decision uncertainty of a first user within the discussion thread is resolved in a subsequent portion of the discussion thread; and in response to determining a resolution of the decision uncertainty of the first user, one or more processors identifying a second user authoring content that influences the first user to resolve the decision uncertainty. 5. The method of claim 4, wherein the decision uncertainty is directed to an opinion. 6. The method of claim 1, wherein the content includes one or more content types of text, audio, images, and video, as included in social network media sites. 7. The method of claim 1, wherein the contextual relationship of the content and the isolated content of the discussion thread, which is determined to be substantially complete is represented in a graphical sequence diagram. 8. The method of claim 1, further comprising: one or more processors defining a confidence level that a particular content segment is related to another content segment, wherein the confidence level is based on a probability threshold; one or more processors determining a probability that a contextual relationship exists between content from different sources, based on an analysis in which the content from different sources is parsed into content segments, wherein each content segment is compared with each other content segment to determine the probability of a contextual relationship between the content segments, and wherein natural language processing, semantic analysis, and sentiment analysis techniques are applied to the content segments to determine an author, topic, and temporal sequence of the content segments; and in response to determining the probability of a contextual relationship between two or more content segments exceeds the probability threshold of the confidence level, one or more processors determining the two or more content segments to be related. 9. A computer program product, comprising: one or more computer readable storage devices, and program instructions stored on the one or more computer readable storage devices, the program instructions comprising: program instructions to gather content authored by a plurality of users over distributed network media sources, and isolated content authored by one or more users of the plurality of users, wherein the content and isolated content are directed to a plurality of topics; program instructions to parse the content and the isolated content; program instructions to aggregate the content that is parsed and the isolated content that is parsed, which are directed to a topic of the plurality of topics, wherein the content that is parsed and the isolated content that is parsed, which are aggregated, form at least a portion of a discussion thread; program instructions to identify a set of users of the plurality of users, authoring the content that is parsed and the isolated content that is parsed, which are directed to the topic; program instructions to determine a probability of whether the discussion thread is at least substantially complete, based on a set of metrics of behavioral patterns of the plurality of users, identified by analysis of historical content and isolated historical content authored by the plurality of users; and in response to determining the probability of the discussion thread to exceed a probability threshold indicating the discussion thread to be at least substantially complete, program instructions to perform a contextual relationship analysis between instances of the content that is parsed and instances of the isolated content that is parsed, which are directed to the topic, and are authored by the set of users of the plurality of users. 10. The computer program product of claim 9, further comprising: program instructions to gather historical content authored by the plurality of users over distributed network sources, wherein the historical content includes historical social media content and historical isolated content, and wherein the historical content and historical isolated content includes a plurality of topics; program instructions to parse the historical content and historical isolated content into a plurality of historical content segments, each historical content segment of the plurality of historical content segments associated with a topic of the plurality of topics and a user of the plurality of users; and program instructions to determine a set of behavioral metrics for each user of the plurality of users, based on activities of contribution with respect to each topic of the plurality of topics, on the historical content and historical isolated content, wherein a probability of a user adding additional content to a discussion thread is derived from the set of behavioral metrics for each user of the plurality of users. 11. The computer program product of claim 10, further comprising: program instructions to create a psycholinguistic profile of each user of the plurality of users, which includes personality attributes and behavioral traits by an analysis of words and phrases used in social media interactions of the historical content authored by the plurality of users; and program instructions to apply the psycholinguistic profile for each of the plurality of users to aggregated content segments of a discussion thread to determine, at least in part, a probability that the discussion thread is substantially complete. 12. The computer program product of claim 9, further comprising: program instructions to track content authored by each user of the discussion thread to determine whether a decision uncertainty of a first user within the discussion thread is resolved in a subsequent portion of the discussion thread; and in response to determining a resolution of the decision uncertainty of the first user, program instructions to identify a second user authoring content that influences the first user to resolve the decision uncertainty. 13. The computer program product of claim 12, wherein the decision uncertainty is directed to an opinion. 14. The computer program product of claim 9, wherein the contextual relationship of the content and the isolated content of the discussion thread, which is determined to be substantially complete, is represented in a graphical sequence diagram. 15. The computer program product of claim 9, further comprising: program instructions to define a confidence level that a particular content segment is related to another content segment, wherein the confidence level is based on a probability threshold; program instructions to determine a probability that a contextual relationship exists between content from different sources, based on an analysis in which the content from different sources is parsed into content segments, wherein each content segment is compared with each other content segment to determine the probability of a contextual relationship between the content segments, and wherein natural language processing, semantic analysis, and sentiment analysis techniques are applied to the content segments to determine an author, topic, and temporal sequence of the content segments; and in response to determining the probability of a contextual relationship between two or more content segments exceeds the probability threshold of the confidence level, program instructions to determine the two or more content segments to be related. 16. A computer system, comprising: one or more computer processors, one or more computer readable storage devices, program instructions stored on the computer readable storage devices for execution by at least one of the one or more processors, the program instructions comprising: program instructions to gather content authored by a plurality of users over distributed network media sources, and isolated content authored by one or more users of the plurality of users, wherein the content and isolated content are directed to a plurality of topics; program instructions to parse the content and the isolated content; program instructions to aggregate the content that is parsed and the isolated content that is parsed, which are directed to a topic of the plurality of topics, wherein the content that is parsed and the isolated content that is parsed, which are aggregated, form at least a portion of a discussion thread program instructions to identify a set of users of the plurality of users, authoring the content that is parsed and the isolated content that is parsed, which are directed to the topic; program instructions to determine a probability of whether the discussion thread is at least substantially complete, based on a set of metrics of behavioral patterns of the plurality of users, identified by analysis of historical content and isolated historical content authored by the plurality of users; and in response to determining the probability of the discussion thread to exceed a probability threshold indicating the discussion thread to be at least substantially complete, program instructions to perform a contextual relationship analysis between instances of the content that is parsed and instances of the isolated content that is parsed, which are directed to the topic, and are authored by the set of users of the plurality of users. 17. The computer system of claim 16, further comprising: program instructions to receive historical content authored by the plurality of users over distributed network sources, wherein the historical content includes historical social media content and historical isolated content, and wherein the historical content and historical isolated content includes a plurality of topics; program instructions to parse the historical content and historical isolated content into a plurality of historical content segments, each historical content segment of the plurality of historical content segments associated with a topic of the plurality of topics and a user of the plurality of users; and program instructions to determine a set of behavioral metrics for each user of the plurality of users, based on activities of contribution with respect to each topic of the plurality of topics, on the historical content and historical isolated content, wherein a probability of a user adding additional content to a discussion thread is derived from the set of behavioral metrics for each user of the plurality of users. 18. The computer system of claim 17, further comprising: program instructions to create a psycholinguistic profile of each user of the plurality of users, which includes personality attributes and behavioral traits by an analysis of words and phrases used in social media interactions of the historical content authored by the plurality of users; and program instructions to apply the psycholinguistic profile for each of the plurality of users to aggregated content segments of a discussion thread to determine, at least in part, a probability that the discussion thread is substantially complete. 19. The computer system of claim 16, further comprising: tracking content authored by each user of the discussion thread to determine whether a decision uncertainty of a first user within the discussion thread is resolved in a subsequent portion of the discussion thread; and in response to determining a resolution of the decision uncertainty of the first user, identifying a second user authoring content that influences the first user to resolve the decision uncertainty. 20. The computer system of claim 16, further comprising: program instructions to define a confidence level that a particular content segment is related to another content segment, wherein the confidence level is based on a probability threshold; program instructions to determine a probability that a contextual relationship exists between content from different sources, based on an analysis in which the content from different sources is parsed into content segments, wherein each content segment is compared with each other content segment to determine the probability of a contextual relationship between the content segments, and wherein natural language processing, semantic analysis, and sentiment analysis techniques are applied to the content segments to determine an author, topic, and temporal sequence of the content segments; and in response to determining the probability of a contextual relationship between two or more content segments exceeds the probability threshold of the confidence level, program instructions to determine the two or more content segments to be related.