Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance

Abstract

Social media have become an integral part of life for many individuals, and social media websites generate incredible amounts of data on a variety of societal topics. Furthermore, some social media posts contain geolocation information, so social media data can be viewed as a spatiotemporal phenomenon. To understand spatiotemporal trends in ultra-large sample social media data, we propose a novel application of the Smoothing Spline Analysis of Variance (SSANOVA) framework, which is a nonparametric approach capable of discovering latent functional relationships in noisy data. Unlike currently available approaches, our proposed SSANOVA framework (a) makes few assumptions about the nature of the spatiotemporal trend, (b) provides a mean of assessing the uncertainty of the estimated spatiotemporal trend, and (c) is scalable to analyze massive samples of social media data. To demonstrate the potential of our approach, we model the daily spatiotemporal Twitter trend in the United States. Our results reveal that the proposed SSANOVA approach can provide accurate and informative estimates of spatiotemporal social media trends, as well as useful information about the precision of the estimated spatiotemporal trends.