Twitter and Reddit Data on COVID-19 and Climate Change, 2019-2021: Language Toxicity, Scientific and Policy Content, and Climate Obstruction

Overview This repository contains data and code needed to reproduce the analysis results from Narimanzadeh et al. 2026. "Incivility in Public Health Policy Discussions Spills Over to Public Engagement with Climate Issues: Evidence from English Language Social Media," Environmental Research Letters. It is a study about spillover of affective polarization from the public health domain to the climate change domain on English-language Twitter and Reddit. The data sets are about English-language social media posts from Twitter and Reddit from Feb. 2019 to Aug. 2021. Information contained include language toxicity, COVID-19 classification, presence of references to climate science and scientists, and conversation structure. For details about the data, please see the published paper. To reproduce analysis from the article, use the code in narimanzadeh_etal_erl_spillover_replication.zip. Data Set Description climate_twitter_general.csv: post-level data set where each row is a tweet about climate change (Feb. 1, 2019 to Aug. 26, 2021) Columns in this data set are as follows: tweet_id: identifier from Twitter that can be used to rehydrate the tweet created_at: datetime string for creation of tweet author_id: identifier from Twitter conversation_id: the tweet_id of the first tweet in the conversation replied_to: tweet_id of the tweet that was replied to user_active_before_covid: whether any of this user's tweets appears before the COVID-19 period user_active_after_covid: whether any of this user's tweets appears after the COVID-19 period toxicity: probability, as returned by Perspective API insult: probability, as returned by Perspective API acard_score: climate misinformation score, as returned by the Augmented Computer Assisted Recognition of Denial and Skepticism (CARDS) model [*]_keywords: whether the tweet contains keywords from different topic sets, including COVID-19, international organizations, scientists; see Section S3 in the supplementary materials for details; note that "has_covid_keywords" includes vaccine-related keywords whereas "covid_keywords" does not root: whether the tweet is the first of a conversation day: day of the data set, starting from Feb 1, 2019 week: week of the data set, starting from week of Feb 1-7, 2019 month: in YYYY-MM format top100/top250: whether the tweet contains one of the 1-100/101-250 most common hashtags in the data set nonus100/nonus250: whether the tweet contains one of the 1-100/101-250 most common explicitly non-U.S.-related hashtags in the data set climate_twitter_scientific.csv: post-level data set, where each row is a tweet about climate science (Feb. 1, 2019 to Aug. 26, 2021) Same columns as climate_twitter_general.csv, but does not contain hashtag-related labels. climate_reddit.csv: post-level data set, where each row is a Reddit comment; based on The Reddit Climate Change Dataset (Feb. 1, 2019 to Aug. 26, 2021) Same columns as climate_twitter_general.csv, but has subreddit names, and does not contain Twitter-related data (i.e., hashtag-related and Twitter IDs) or climate misinformation scores. climate_threads_general.csv: conversation-based data set, where each row is a tweet from a conversation chain with the first tweet in the chain is about climate change (Feb. 1, 2019 to Aug. 31, 2021) This data set does not contain tweet IDs, as each row is specified by the ID of the root tweet (the first tweet in the chain), ID of the leaf tweet (the final tweet in the chain after all disqualified tweets are removed), and the tweet's depth in the chain. Consecutive tweets in the chain made by the same account are collapsed into one row (and therefore one depth). The toxicity values are calculated by concatenating the tweets' texts (with two newline characters between each individual tweet text) and passing the entire text to Perspective API. Individual tweet that appear in more than one chain (e.g., root tweets appear in all chains leading to different leaves of the conversation tree) are represented by multiple rows in the data set. Columns in the data set can be designated as current, predecessor, or root, which indicates whether the variable is about the the tweet represented by the row (current), the tweet being responded to by the current tweet (predecessor) and the first tweet in the chain that is about climate change (root). IMPORTANT: Data set should be used by subsetting to only is_all_english = 0 or is_all_english = 1 as they share many duplicated entries (see is_all_english below). Columns in this dataset are as follows: root_id: the tweet ID of the first tweet in the conversation chain leaf_id: the tweet ID of the last tweet in the conversation chain current_depth: the depth of the tweet in the conversation chain, specified by root_id and leaf_id [*]_toxicity: toxicity probability of the root/current/predecessor tweet (from Perspective API) [*]_keywords: whether the root/current/predecessor tweet contains keywords from different topic sets, including COVID-19, international organizations, scientists; see Table S3.1 in the supplementary materials for details; note that COVID-19 and international organization categories are broken down into smaller sub-categories [*]_created_at: datetime string for creation of root/current/predecessor tweet; if row represents multiple collapsed tweets from the same author, this indicates datetime of the first tweet in the collapsed set current_author_id: identifier from Twitter tweets_within_hour: number of tweets in the entire conversation tree (not limited to this chain) under the root tweet within first hour is_all_english: two types of language pruning exists in this data set; rows with is_all_english = 1 are pruned by removing all non-English posts (agreed by both Twitter and Perspective), while is_all_english = 0 removes only conversations where root tweet is non-English climate_threads_scientific.csv: conversation-based data set, where each row is a tweet from a conversation chain with the first tweet in the chain is about climate science (Jan. 21, 2019 to Aug. 31, 2021) Same notes as climate_threads_general.csv, with the exception that root tweets all mention or link to scientific publications on climate change. daily_climate_series.csv: day-level data set with daily toxicity probability averages of different data subsets (Feb. 1, 2019 to Aug. 26, 2021) Used to reproduce time series analysis in Fig. 1.A of the article. Reference Please reference the original study when using this data set. Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova, Ted Hsuan Yun Chen. 2026. "Incivility in Public Health Policy Discussions Spills Over to Public Engagement with Climate Issues: Evidence from English Language Social Media." Environmental Research Letters.