An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and/or first responders in the affected area.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-11. (canceled) 12. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; a set of instructions stored in the memory and executed by at least one of the processors to predict an event occurrence based on a question presented to the system, wherein the set of instructions perform actions of: receiving, by the system, a first question from a first user; generating, by the system, a plurality of context parameters for the first question; generating, by the system, one or more profile parameters for the first user; generating, by the system, one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the first question; generating, by the system, a ranked plurality of event occurrences based on the plurality of context parameters, the one or more profile parameters for the first user, and the one or more historical questions, answers, and events; predicting an event occurrence from the ranked plurality of event occurrences based on rank; and sending, in near-real time to submission of the first question, a notification message to at least the first user or a first responder which includes the predicted event occurrence. 13. The information handling system of claim 12, wherein generating the plurality of context parameters for the first question comprises performing, by the system, a natural language processing (NLP) analysis of the first question, wherein the NLP analysis extracts key terms, question sentiment, question focus, and lexical answer type information, from the first question. 14. The information handling system of claim 12, wherein generating one or more profile parameters for the first user comprises identifying, by the system, authorship profile information for the first user, a first user location and time information for when the first question was submitted. 15. The information handling system of claim 12, wherein generating one or more historical questions, answers, and events comprises performing, by the system, a machine learning approach for identifying from a database one or more historical questions and historical answers having a specified spatial and/or temporal proximity to the first question. 16. The information handling system of claim 12, wherein generating one or more historical events comprises performing, by the system, a probabilistic or vector space model-based search for identifying one or more distress events from an event database that have a specified spatial and/or temporal proximity to the first question. 17. The information handling system of claim 12, wherein generating the ranked plurality of event occurrences comprises performing, by the system, a natural language processing (NLP) to execute a machine learning classification model based on model inputs comprising location information for the first user, profile attributes for the first user, submission timing information for the first question, question sentiment, question focus, and lexical answer type information for the first question. 18. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the system to predict an event occurrence based on a question presented to the system by performing actions comprising: receiving, by the system, a first question from a first user; generating, by the system, a plurality of context parameters for the first question; generating, by the system, one or more profile parameters for the first user; generating, by the system, one or more historical questions, answers, and events having a specified spatial and/or temporal proximity to the first question; generating, by the system, a ranked plurality of event occurrences based on the plurality of context parameters, the one or more profile parameters for the first user, and the one or more historical questions, answers, and events; predicting an event occurrence from the ranked plurality of event occurrences based on rank; and sending, in near-real time to submission of the first question, a notification message to at least the first user or a first responder which includes the predicted event occurrence. 19. The computer program product of claim 18, wherein generating the plurality of context parameters for the first question comprises performing, by the system, a natural language processing (NLP) analysis of the first question, wherein the NLP analysis extracts key terms, question sentiment, question focus, and lexical answer type information, from the first question. 20. The computer program product of claim 19, wherein generating the ranked plurality of event occurrences comprises performing, by the system, a natural language processing (NLP) to execute a machine learning classification model based on model inputs comprising location information for the first user, profile attributes for the first user, submission timing information for the first question, key terms, question sentiment, question focus, and lexical answer type information for the first question.