Nonparametric density-based procedure of detecting emerging events from social media. - Castillo, Louise Adrian D. (University of the Philippines, 2022)
ABSTRACT Detecting emerging events early enough is key to mitigation, prevention and containment of events especially those related to disease outbreaks, natural disasters, public safety, among others. Most tests for goodness of fit focuses on the central location of the density function, missing to detect changes that usually occur at the tails of the distribution. Oftentimes, these methods are prone to false negative results. We proposed two methods of discovering emerging events based on nonparametric density estimates from a date-generating process reflecting those in social media text data. The first method compares the percentile values between the baseline and speculated distribution while the second algorithm modifies the Kolmogorov Smirnov test to focus on the upper tails of the distribution. Simulation studies suggest that both algorithms can detect emerging events specially in case with large number of data points.