Extracting Diurnal Patterns of Real World Activity from Social Media


  • Nir Grinberg Rutgers University
  • Mor Naaman Rutgers University
  • Blake Shaw Foursquare Inc.
  • Gilad Lotan SocialFlow Inc.




Social Media, Twitter, Foursquare, Temporal Modeling, Diurnal Patterns, NLP


In this study, we develop methods to identify verbal expressions in social media streams that refer to real-world activities. Using aggregate daily patterns of Foursquare checkins, our methods extract similar patterns from Twitter, extending the amount of available content while preserving high relevance. We devise and test several methods to extract such content, using time-series and semantic similarity. Evaluating on key activity categories available from Foursquare (coffee, food, shopping and nightlife), we show that our extraction methods are able to capture equivalent patterns in Twitter. By examining rudimentary categories of activity such as nightlife, food or shopping we peek at the fundamental rhythm of human behavior and observe when it is disrupted. We use data compiled during the abnormal conditions in New York City throughout Hurricane Sandy to examine the outcome of our methods.




How to Cite

Grinberg, N., Naaman, M., Shaw, B., & Lotan, G. (2021). Extracting Diurnal Patterns of Real World Activity from Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 205-214. https://doi.org/10.1609/icwsm.v7i1.14409