Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
This work addresses the challenge of understanding public opinion shifts on climate change for social scientists and policymakers, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of tracking changes in Twitter users' climate change sentiment in response to natural disasters, using limited labeled data to classify tweets with over 75% accuracy and finding that hurricanes in 2018 led to a statistically significant increase in climate change acceptance, while blizzards and wildfires did not.
While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences. On the sub-population of Twitter users, we examine whether climate change sentiment changes in response to five separate natural disasters occurring in the U.S. in 2018. We begin by showing that relevant tweets can be classified with over 75% accuracy as either accepting or denying climate change when using our methodology to compensate for limited labeled data; results are robust across several machine learning models and yield geographic-level results in line with prior research. We then apply RNNs to conduct a cohort-level analysis showing that the 2018 hurricanes yielded a statistically significant increase in average tweet sentiment affirming climate change. However, this effect does not hold for the 2018 blizzard and wildfires studied, implying that Twitter users' opinions on climate change are fairly ingrained on this subset of natural disasters.