Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States

Predictive model We employ statistical learning methods to develop a predictive model relating Twitter discourse to climate opinion survey responses. The model takes the previously discussed twitter topic dis- tributions as inputs to predict climate change opinions, as discussed in more detail below. Response variable—first principal component, CCO. The Yale Climate Opinion Dataset [1] contains a variety of responses on various issues related to climate change, ranging from trust in scientists to opinions on renewable energy policies. Rather than developing separate models for each response, we leverage dimensionality reduction techniques and consolidate all of the survey responses into a single variable using Principal Component Analysis (S3 Fig in S1 File). The first principal component accounts for 89% of the variance in the data, indicating that the survey responses are highly correlated. This is not surprising since the survey questions speak to people’s beliefs related to climate change; those that believe climate change is a press- ing issue are also likely to believe that policy addressing these issues is important. Because of the extremely high correlation between variables, it is reasonable to consolidate them into a single index which we coin as “Consolidate Climate Opinion (CCO)”. We posit that CCO represents the general degree of concern for the environment and climate change related issues. Fig 1a displays tweet frequency across the nation based on climate-rele- vant Twitter activity collected over this period. Fig 1b shows geographic variation in climate

attitudes. Based on the data used from the Yale Climate Opinion Survey (S2 Table in S1 File), regions with higher CCO values have residents who are more worried about climate change and its impacts as well as more likely to support pro-environmental regulations and policies. Predictors—topic distributions. To develop topic distributions for each county, we pro- cess tweets from the regional corpus using the topic model developed from the full corpus. For each county in the U.S., we filter the dataset for tweets associated with that county. We then preprocess each tweet in the filtered subset is then preprocessed in the manner described ear- lier. The output of an LDA topic model is a set of numbers which represent the probability that a given word from each tweet belongs in each topic. We then sum this set of numbers is then summed across all of the words to derive a so called “topic distribution” for the tweet. To create the final county distribution, we sum topic distributions for all tweets in a county. We normalize each county distribution by dividing by its sum such that the sum of the final county distribution is one, allowing for comparison between counties. The analysis excludes counties with four or fewer tweets as the distributions generated from such a small number of tweets are not accurate representations of the county’s beliefs. Fig 1. Distribution of Consolidated Climate Opinion (CCO) and Twitter users in the United States. (a) County-level CCO,

calculated as the principal component reduction of 14 survey questions collected in the Yale Climate Opinion Dataset [1]. (b) County-level distribution of Tweet Density (tweets / person, log scale) in the United States based on climate-related tweets collected using the Twitter API from April 18th through April 25th, 2019. https://doi.org/10.1371/journal.pone.0245319.g001 PLOS ONE An approach for augmenting surveys with social media to understand climate opinion in the United States PLOS ONE | https://doi.org/10.1371/journal.pone.0245319 January 14, 2021 7 / 16