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

where query is the specific keywords being targeted, excluding specifies words to exclude from the results, count identifies the number of tweets to return in the query (100 is the maxi- mum), tweet_mode describes whether or not data beyond the tweet itself should be returned, and max_id specifies the most recent tweet ID to be used in the query (allowing us to exclude tweets that have already been collected, as tweet IDs are issued chronologically. After data acquisition, the final topic corpus includes roughly 350,000 tweets. The regional corpus consists of geo-located tweets that match the aforementioned criteria. The most precise form of geo-tagging is a tweet that includes the latitude and longitude where the tweet origi- nated, but less than 1% of tweets include this information. Another form of geo-tagging is a tweet that is associated with a specific location or a tweet that originates from a user associated with a specific location (e.g. Chicago, Illinois). For these tweets to be returned by the query, the entire region as specified by Twitter must be encompassed by the search radius. To per- form geo-tagging, the search criteria is updated to include geographic coordinates and a search radius as follows: where latitude and longitude specify the geographic center of a county and radius is the radius from the center in which to look for tweets. For the tweet to be returned by the query, it must be geo-tagged. The final regional corpus includes 190,000 tweets. Topic modeling Latent Dirichlet Allocation (LDA). To extract the key topics in the corpora of Twitter

data, we utilize an unsupervised clustering technique called Latent Dirichlet Allocation (LDA). Latent Dirichlet Allocation (LDA) has been used extensively to detect topics on Twitter [20]. LDA is a probabalistic topic model, a class of Bayesian latent variable models. At a high level, it represents documents as a mixture of topics which are in turn represented by a set of words [21]. Given a corpus of documents, an LDA model learns the topic representation of each doc- ument and the words associated with each topic. Once a model is trained, given a document, the model will produce a topic likelihood distribution which identifies the relevant topic(s) in a document. For our analysis, we use a popular Natural Language Processing (NLP) Python package, Gensim [22] with the Machine Learning for Language Toolkit (MALLET) imple- mentation [23]. query = (global warming) or (climate) or (carbon) excluding (monoxide) and (RT) count = 100 tweet_mode = extended max_id = [most recent tweet id] geo-code = latitude, longitude, radius 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 4 / 16