Connecting social media data with observed hybrid data for environment monitoring

Jinyan Chen, Sen Wang, Bela Stantic

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Environmental monitoring has been regarded as one of effective solutions to protect our living places from potential risks. Traditional methods rely on periodically recording assessments of observed objects, which results in large amount of hybrid data sets. Additionally public opinions regarding certain topics can be extracted from social media and used as another source of descriptive data. In this work, we investigate how to connect and process the public opinions from social media with hybrid observation records. Particularly, we study Twitter posts from designated region with respect to specific topics, such as marine environmental activities. Sentiment analysis on tweets is performed to reflect public opinions on the environmental topics. Additionally two hybrid data sets have been considered. To process these data we use Hadoop cluster and utilize NoSql and relational databases to store data distributed across nodes in share nothing architecture. We compare the public sentiments in social media with scientific observations in real time and show that the “citizen science” enhanced with real time analytics can provide avenue to nominatively monitor natural environments. The approach presented in this paper provides an innovative method to monitor environment with the power of social media analysis and distributed computing.

Original languageEnglish
Pages (from-to)125-135
Number of pages11
JournalStudies in Computational Intelligence
Volume737
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Hybrid data
  • Sentiment analysis
  • Social media

ASJC Scopus subject areas

  • Artificial Intelligence

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