A computer implemented method, computer program product and computer system for crowd detection. The computer system (1000) receives through an interface (1006) a plurality of user generated data records from a social media data storage (SMDS1, SMDS2) component, wherein a user generated data record comprises a text portion. A location extractor (1001) extracts location information from a subset of the user generated data records being associated with geographic locations. A time identifier (1002) identifies in the subset time information being associated with the extracted location information. A trained machine learning system (1004) an indicator for crowd formation, wherein the indicator is an output of the machine learning system in response to an input pair of associated location information and time information.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for crowd prediction, comprising: receiving through an interface a plurality of user generated data records from a social media data storage component, wherein a user generated data record comprises a user-generated text portion; extracting location information from user-generated text portions of a subset of the user generated data records being associated with geographic locations; identifying in the subset time information being associated with the extracted location information; and detecting an indicator for crowd formation based on the extracted location and time information, using a machine learning system component. 2. The computer implemented method of claim 1, wherein a specific user generated data record being associated with a specific geographic location has a location association, which has a certain location reliability and the extracted location information is tagged with a location confidence score dependent on the respective location reliability. 3. (canceled) 4. The computer implemented method of claim 1, further comprising: detecting, with the machine learning system component, a further indicator for crowd movement, wherein the further indicator is a further output of the machine learning system in response to an input pair of pairs of associated location information and time information. 5. The computer implemented method of claim 1, further comprising: generating an event if the indicator for crowd formation exceeds a predefined threshold. 6. The computer implemented method of claim 1, wherein the machine learning system further uses for detecting crowd formation anyone of the following feature groups: information from a background model having data of how often certain location information is commonly mentioned in text portions of user generated data records within a predefined time interval; information about crowd formation at the mentioned location information in the past; and user profile information. 7. The computer implemented method of claim 1, wherein identifying time information comprises: parsing the text portion of each data record of the subset; and generating a plurality of associated data triples, each associated data triple having associated location information, time information and user generated data record information. 8. The computer implemented method of claim 1, wherein extracting location information comprises: deriving location information from a non-location entity in the text portion of a user generated data record. 9. (canceled) 10. A computer program product that when loaded into a memory of a computing device and executed by at least one processor of the computing device executes the steps of a computer implemented method for crowd prediction comprising: receiving through an interface a plurality of user generated data records from a social media data storage component, wherein a user generated data record comprises a user-generated text portion; extracting location information from user-generated text portions of a subset of the user generated data records being associated with geographic locations; identifying in the subset time information being associated with the extracted location information; and detecting an indicator for crowd formation based on the extracted location and time information, using a machine learning system component. 11. A computer system for detection of crowd formation according to a method comprising: receiving through an interface a plurality of user generated data records from a social media data storage component, wherein a user generated data record comprises a user-generated text portion; extracting location information from user-generated text portions of a subset of the user generated data records being associated with geographic locations; identifying in the subset time information being associated with the extracted location information; and detecting an indicator for crowd formation based on the extracted location and time information, using a machine learning system component; wherein the system comprising: an interface component configured to receive the plurality of user generated data records; a location extractor component configured to extract the location information; a time identifier component configured to identify in the subset the time information being associated with the extracted location information; and a trained machine learning system component configured to detect an indicator for crowd formation. 12. (canceled) 13. (canceled) 14. (canceled) 15. (canceled) 16. The method of claim 1 in which the time information is extracted from user-generated text portions of the subset of data records. 17. The method of claim 1 or claim 16 in which the indicator is output in response to an input pair of associated location and time information. 18. The method of claim 16 in which the indicator is detected when at least a predefined number of user generated data records of the subset falls within a respective environment around the input pair. 19. The method of claim 17 or claim 18 in which the input pair is derived from one or more of: data input via a graphical user interface; new user records received from the social media storage component; and most frequent locations mentioned in user generated data records. 20. The computer implemented method of claim 2, wherein the location confidence score is above a predefined threshold if location information can be derived by matching a text portion of the data record with a domain specific gazetteer entry specifying a respective geographic location. 21. The computer implemented method of claim 2 or 20, wherein the specific user generated data record with the location association has a time association, which has a certain time reliability and the identified time information is tagged with a time confidence score dependent on the respective time reliability. 22. The computer program product of claim 10, wherein at least one of the following holds true: the time information is extracted from user-generated text portions of the subset of data records; the indicator is output in response to an input pair of associated location and time information; the indicator is detected when at least a predefined number of user generated data records of the subset falls within a respective environment around the input pair; the input pair is derived from one or more of: data input via a graphical user interface, new user records received from the social media storage component, and most frequent locations mentioned in user generated data records; a specific user generated data record being associated with a specific geographic location has a location association, which has a certain location reliability and the extracted location information is tagged with a location confidence score dependent on the respective location reliability; the location confidence score is above a predefined threshold if location information can be derived by matching a text portion of the data record with a domain specific gazetteer entry specifying a respective geographic location; the specific user generated data record with the location association has a time association, which has a certain time reliability and the identified time information is tagged with a time confidence score dependent on the respective time reliability; the method further comprising: detecting, with the machine learning system component, a further indicator for crowd movement, wherein the further indicator is a further output of the machine learning system in response to an input pair of pairs of associated location information and time information; the method further comprising: generating an event if the indicator for crowd formation exceeds a predefined threshold; the machine learning system further uses for detecting crowd formation anyone of the following feature groups: information from a background model having data of how often certain location information is commonly mentioned in text portions of user generated data records within a predefined time interval, information about crowd formation at the mentioned location information in the past, and user profile information; identifying time information comprises: parsing the text portion of each data record of the subset, and generating a plurality of associated data triples, each associated data triple having associated location information, time information and user generated data record information; extracting location information comprises: deriving location information from a non-location entity in the text portion of a user generated data record. 23. The computer system of claim 11, wherein at least one of the following holds true: the time information is extracted from user-generated text portions of said subset of data records; the indicator is output in response to an input pair of associated location and time information; the indicator is detected when at least a predefined number of user generated data records of the subset falls within a respective environment around the input pair; the input pair is derived from one or more of: data input via a graphical user interface, new user records received from the social media storage component, and most frequent locations mentioned in user generated data records; a specific user generated data record being associated with a specific geographic location has a location association, which has a certain location reliability and the extracted location information is tagged with a location confidence score dependent on the respective location reliability; the location confidence score is above a predefined threshold if location information can be derived by matching a text portion of the data record with a domain specific gazetteer entry specifying a respective geographic location; the specific user generated data record with the location association has a time association, which has a certain time reliability and the identified time information is tagged with a time confidence score dependent on the respective time reliability; the method further comprising: detecting, with the machine learning system component, a further indicator for crowd movement, wherein the further indicator is a further output of the machine learning system in response to an input pair of pairs of associated location information and time information; the method further comprising: generating an event if the indicator for crowd formation exceeds a predefined threshold; the machine learning system further uses for detecting crowd formation anyone of the following feature groups: information from a background model having data of how often certain location information is commonly mentioned in text portions of user generated data records within a predefined time interval, information about crowd formation at the mentioned location information in the past, and user profile information; identifying time information comprises: parsing the text portion of each data record of the subset, and generating a plurality of associated data triples, each associated data triple having associated location information, time information and user generated data record information; extracting location information comprises: deriving location information from a non-location entity in the text portion of a user generated data record.