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

Nathaniel E. Helwig, Yizhao Gao, Shaowen Wang, Ping Ma

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)491-504
Number of pages14
JournalSpatial Statistics
StatePublished - Nov 1 2015

Bibliographical note

Funding Information:
The project was partially supported by NSF DMS 1438957 , DMS 1440037 , DMS 1440038 , ACI-1443080 , BCS-0846655 , IIS-1354329 . The project was also partially supported by the NCSA/IACAT fellowship and start-up funds from the University of Minnesota ( 10986-20042 ).

Publisher Copyright:
© 2015 Elsevier Ltd.


  • Smoothing spline
  • Social media
  • Spatial smoothing
  • Spatiotemporal smoothing


Dive into the research topics of 'Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance'. Together they form a unique fingerprint.

Cite this