Integrating Geospatial Data and Social Media in Bidirectional Long-Short Term Memory Models to Capture Human Nature Interactions

Contact with nature has been linked to human health, but little information is available for how individuals utilize urban nature. We developed a bidirectional long short-term memory model for classifying whether tweets describe the proposed pathways through which nature influences health: exercise, aesthetic stimulation, stress reduction, safety, air pollution mediation, and/or social interaction. To adjust for regional variations in urban nature context, we integrated OpenStreetMap data on nature and non-nature features for each long-short term memory cell. Training (n = 63073), development (n = 5000), and test (n = 5000) sets consisted of labeled tweets from Portland, Oregon. Tweets from New York City (NYC) (n = 5000) were also labeled to test generalizability. The model was applied retrospectively to 20 million tweets from 2017 and continuously to Meetup posts for 7,708 cities in North America. F1Scores ranged from 0.54 to 0.82 in the NYC dataset, a 24% to 92% improvement over current methods. Precision ranged from 0.58 to 0.83, while recall ranged from 0.39 to 0.81. Adding OpenStreetMap features led to greater percent and absolute F1Scores in NYC compared to Portland. Average F1Scores were greater in models with a nature label in addition to human behavior labels (0.59 vs. 0.65), suggesting health behaviors are influenced by urban nature.