A Probabilistic Model of Real Time Event Detection and Reporting

-A probabilistic model provides a way to detect multiple instances of real time events and to estimate the location of targeted event like earthquakes, typhoons, traffic jams. For this, two models have been proposed named temporal and spatial models to detect real time events and estimate the targeted event locations respectively by dealing with sensor reading appropriately. our work is based on the twitter-which is used to deal with sensor reading appropriately real time events and particularly for location estimation. An important characteristic of twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we device a classifier of tweets based on features such as the keywords in a tweet, the number of words and their context, by using MI-SVM . We regard each twitter user as a sensor and apply particle filtering .which are widely used for location estimation. The particle filter works better than other other comparable methods for estimating the locations of target events. In a temporal model, each tweet has its own post time, when target event occurs. Depends on quantities of tweets, target event behavior can be analyzed. This distribution is apparently an exponential distribution i.e. 0.34 on average, on which data fit very well. In spatial model, each tweet, that is associated with a location and by using probabilistic approximation algorithm called” particle filter”, can estimate the location of event occurred from sensor readings. Particle filtering works better than other comparable methods for estimating the location of events. Finally, we develop a probabilistic model for real time event detection and reporting system using event detection algorithm. Keywords--Social networking, data mining, SVM, tweet analysis.