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

We note there are a significant number of different methods for extracting topics from large text corpora, each with their own advantages and shortcomings. In the case of LDA, the algorithm assumes that each document contains multiple topics. However, in the case of tweets, which are likely to contain a limited number of topics due to character restrictions, this may not be a reasonable assumption. Previous research has proposed a modified LDA algo- rithm, namely Twitter-LDA, which assumes that each tweet contains exactly one topic [20]. In a comparison between LDA and Twitter-LDA on unfiltered Twitter data, where the number of topics was set at 110, the Twitter-LDA outperformed standard LDA [20]. However, the rationale for using standard LDA in this study is two-fold: (1) the goal of the LDA analysis is to derive topic distributions for distinct geographic regions; as this is an aggregate measure, by assuming that each tweet contained exactly one topic, we would limit the components of the topic distribution, potentially reducing the accuracy of the overall distribution, and (2) because the data is filtered to only pertain to climate change, the topics identified are likely to have more overlap than in a general tweet corpus and thus it was not reasonable to assume that each tweet would only contain a single topic. Here we utilize LDA as a topic model, but we emphasize that the approach presented in this study is not dependent on that specific method. In summary, the choice of the algorithm should be guided by the research question and avail- able data.

Data preprocessing. Latent Dirichlet Allocation (LDA) requires preprocessing each of the incoming documents. We begin this process by removing punctuation, URLs, and men- tions (i.e. users referencing other users) from tweets. Though this information can be impor- tant for certain applications with Twitter data, it offers little value in the development of a topic model. After removing these features, we convert the remaining text to lowercase. Next, we remove any stopwords from the document. Stopwords (e.g., “the”, “and”, “are”, etc.) are the most common words in the English language and have little bearing on the overall mean- ing of a document [24]. Additionally, we remove stopwords specific to Twitter discussion. Examples include ‘&’ (the Twitter rendering of the ampersand sign) and ‘RT’ (Twitter code indicating that a tweet is a retweet, or copy, of another user’s tweet). We also remove the words “climate” and “carbon” as well as the phrase “global warming” as those were the key- words contained in the query used to construct the dataset and would otherwise be over-repre- sented in every topic. Finally, we reduce each word to its lemma, or root. This ensures that the algorithm does not incorrectly identify different tenses or forms of a word as separate words. To perform these tasks, we use the the NLTK package in Python [25]. Following this preliminary preprocessing, we take further steps are to increase the efficiency at which the model is developed. Notably, we remove words that occur very frequently and

those that occur infrequently from the set of words (i.e., dictionary) which is used to construct the topic model. Reducing the size of the dictionary, significantly improves the run-time of the algorithm, allowing for rapid testing and iteration. To reduce the size of the dictionary, we first only consider words greater than two characters. We then eliminate words that appeared in greater than 50% of tweets (e.g., “the”) as they would provide little information about the rele- vant topic. Next, we drop words that appeared in fewer than 100 (less than 0.03% of the data- set) of the tweets. Finally, we leverage a randomized subset of the tweets used in preliminary model training for identification of the optimal number of topics. The size of the subset depends on the phase of model development and is specified in the following section. Final topic model. Topic modeling requires user input for determining the optimal num- ber of topics. In a well-performing model, the topics are distinct and intuitive. There are a vari- ety of metrics that can assess different aspects of model quality but there are no formalized methods to make a holistic assessment. One popular metric is coherence, which rewards 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 5 / 16