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

Data Two key sources of data were used in this study: climate change opinion survey data and social media activity, aggregated by US county. Climate opinion survey data were sourced from the Yale Climate Opinion Dataset [1] while social media data (i.e. tweets) were sourced from Twitter. Climate opinion dataset. Survey research is a time-intensive and expensive method for gathering information on public opinion. To combat this challenge, in 2015 Howe et al. [1] developed a model to predict survey responses on a variety of climate related issues. The topics covered in the survey range from risk perceptions to policy preferences. The model provides information at the state, congressional district, metropolitan, and county levels, using a small set of demographic and geographic variables [1]. Based on validation on several independently conducted surveys, the model is reported to be accurate to within seven percentage points: slightly less accurate than the typical three percentage points expected of a traditionally-con- ducted survey [1]. While lower in accuracy, it provides the advantage of access to highly granu- lar and spatially expansive data. For this analysis, the predicted survey responses are used as the response variable, as the granularity of the data allows for mapping the high resolution geo-located social media data to climate opinions. With this data source, climate opinion information can be mapped at the county rather than state level and ultimately provides more data for training and validation of the proposed predictive model.

Twitter data. To capture the topics, themes, and events associated with regional climate opinions, we extract data from Twitter user accounts. Twitter offers two well-documented Application Program Interfaces (APIs) through which data can be accessed. The Standard API allows virtually unrestricted access to tweets produced in the last seven days, with a limit on the number of requests per hour. The Premium API allows users to access the full Twitter archive, but has much more restrictive rate limits. This API is designed for users and organiza- tions who are interested in a paid subscription. In the interest of data accessibility, this analysis relies on the Standard API. By using tweets collected over a week long period, we ensure that all of the information required to conduct the study can be collected without a premium API subscription. One shortcoming of the Stan- dard API is that it is not guaranteed to return every tweet in a given period of time. However, by taking advantage of different parameters in Twitter’s search function (namely specifying the ID of the most recent tweet to retrieve), the majority of tweets within a specific window can be retrieved. In two validation tests conducted over two separate three hour windows, we found that the Standard API returned at least 98% of the tweets retrieved by the Premium API. We utilize two data subsets in training the proposed predictive model: a large corpus to train the topic model (later referred to as the topic corpus) and a smaller, geo-located

corpus to derive regional topic features (referred to as the regional corpus). Because of Twit- ter’s privacy policy, not all tweets can be geo-located, which leads to the reduction in size from the topic to regional corpus. However, as tweets are generally short, it is important to maximize the sample size used to develop the topic model, hence the two distinct corpora. The topic corpus consists of every tweet that matches the keywords “carbon” (excluding “carbon monoxide”), “climate”, or “global warming” (and excludes “RT”, Twitter’s representa- tion of a retweet) in the region of interest (US, in this case) over a seven day period. In this study, the date range is April 18th through April 25th, 2019—notably encompassing Earth Day and the Extinction Rebellion in London. The following is an example query to illustrate how data was collected, modified to enhance readability: 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 3 / 16