Extracting Diurnal Patterns of Real World Activity from Social Media

Nir Grinberg, Mor Naaman, Blake Shaw, Gilad Lotan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

34 Scopus citations

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 20111 International Conference on Weblogs and Social Media (ICWSM)
Pages205-214
ISBN (Electronic)9781577356103
DOIs
StatePublished - Jul 2013
Externally publishedYes

Fingerprint

Dive into the research topics of 'Extracting Diurnal Patterns of Real World Activity from Social Media'. Together they form a unique fingerprint.

Cite this