2018 Conference

Individual Paper Presentation

The scientific community requires empirical evidence on how the public builds understanding of climate science. To date, public opinion research has largely focused on classifying segments of the public by their beliefs and determining how correcting and/or inoculating the public against misinformation can lead to more informed public discourse. Despite a media climate increasingly shaped by influential actors on social media platforms, social science research applying these findings to now ubiquitous Web 2.0 technologies such as YouTube, Twitter, Facebook and their associated live-streaming technologies remains in its infancy. Here we examine how a data mining technique (Word2Vec) pioneered to aid machines in learning how to translate languages can determine what words were most often associated with climate change between September 1 and December 31 of 2017 on Twitter and four prominent climate science blogs. We hypothesized that the withdrawal of the United States from the 2016 Paris Climate Agreement by President Donald Trump several months prior to this work brought a surge in online participation on climate change. We analyzed Twitter posts that included the words “climate change” and then compared them against content on four prominent climate change blogs to uncover the accuracy of the average tweet. Our two research questions asked 1) What concepts are most often associated with the issue of climate change in online Twitter discourse on the topic? and 2) Did the average Twitter post demonstrate increases in scientific accuracy during this time period when compared to those of scientific bloggers? We found that Trump’s withdrawal did lead to a surge in Twitter posts but that it did not necessarily indicate increasing public understanding of the issue. This work holds implications for how to inoculate the public against misinformation online and build digital communities committed to correcting public understandings.