Extracting Diurnal Patterns of Real World Activity from Social Media

By Nir Grinberg, Mor Naaman, Blake Shaw, Gilad Lotan. 2013

63 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Sociology and Political Science, Transportation

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Grinberg et al. (2013) found Foursquare check-ins to exhibit clear weekly patterns for several categories, while Golder and Macy (2011) observed links between the sentiment of tweets and cycles of sleep and seasonality.”
The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters (2015, background): “For example, one study that combines the Sandy Twitter data with Foursquare data found that grocery shopping peaked the night before the hurricane, while nightlife had a significant spike the day afterwards (Grinberg et al. 2013).”
Understanding Short-term Changes in Online Activity Sessions (2017, methodology): “…news aggregator Digg displays clear daily and weekly cycles of activity [25]; moods expressed by Twitter users worldwide show daily and seasonal variation [5]; daily patterns of food consumption, as well as increased nightlife activity on the weekends, emerge from Foursquare check-in data [9].”