Usefulness of Social Sensing Using Text Mining of Tweets for Detection of Autumn Phenology

Nagai Shin, Yasuyuki Maruya, Taku M. Saitoh, Narumasa Tsutsumida

Research output: Contribution to journalArticlepeer-review

Abstract

Can social sensing detect the spatio-temporal variability of autumn phenology? We analyzed data published on the Twitter social media website through the text mining of non-geotagged tweets regarding a forested, mountainous region in Japan. We were able to map the spatial characteristic of tweets regarding peak leaf coloring along an altitudinal gradient and found that text mining of tweets is a useful approach to the in situ collection of autumn phenology information at multiple locations over a broad spatial scale. Potential uncertainties in this approach were examined and compared to other online research sources and methods, including Google Trends and information on widely available websites and live camera images. Finally, we suggest ways to reduce the uncertainties identified within our approach and to create better integration between text mining of tweets and other online research data sources and methods.

Original languageEnglish
Article number659910
JournalFrontiers in Forests and Global Change
Volume4
DOIs
Publication statusPublished - Oct 7 2021

All Science Journal Classification (ASJC) codes

  • Forestry
  • Ecology
  • Global and Planetary Change
  • Nature and Landscape Conservation
  • Environmental Science (miscellaneous)

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