DOI: 10.5061/dryad.79cnp5ht0
title: Twitter data reveal six distinct environmental personas

description: Effective digital environmental communication is integral to galvanizing
 public support for conservation in the age of social media. Environmental
 advocates require messaging strategies suited to social media platforms,
 including ways to identify, target, and mobilize distinct audiences. Here,
 we provide – to the best of our knowledge – the first systematic
 characterization of environmental personas on social media. Beginning with
 1 million environmental nongovernmental organization (NGO) followers on
 Twitter, of which 500,000 users met data quality criteria, we identified
 six personas that differ in their expression of 21 environmental issues.
 General consistency in the proportional composition of personas was
 detected across 14 countries with sufficiently large samples. Within the
 US, although the six personas varied in their mean political ideology, we
 did not observe that the personas split along political party lines. Our
 results pave the way for environmental advocates – including NGOs, public
 agencies, and researchers – to use audience segmentation methods like the
 one discussed here to target and tailor messages to distinct
 constituencies at speed and scale. This repository contains several
 tabular files that can be used to query user data from Twitter or
 reproduce the main results in the main text of the article.
description: Please see the main text of the article and Supplementary
 Information for more details on how the data were gathered and
 processed.
description: These data may only be used for publically accessible research
 and may not be used for private, for-profit use. This replication code and
 dataset accompanies the manuscript linked at:
 https://doi.org/10.1002/fee.2510.
description: ## Replication materials documentation for "Twitter data reveal six
 distinct environmental personas" This replication code and dataset
 accompanies the manuscript linked at: https://doi.org/10.1002/fee.2510. I
 provide a description of the replication datasets below and include a
 `SHA256` checksum that you can use to ensure the integrity of the
 downloaded file (please execute `shasum -a 256 FILENAME` in the command
 line to verify, or use some other utility to find the SHA256 checksum for
 each file). ### Datasets * `MeanViewpoints.tsv` contains the mean and
 standard error of the mean (SEM) for the issue viewpoints shown in Figure
 1 for the six personas in a "long data" format * Columns: *
 variable: name of the environmental issue * mean: persona-level mean
 viewpoint value for that issue * SEM: standard error of the mean *
 Persona: abbreviated name for the six personas (SMA: Smart alecks, GEN:
 Generalists, STE: Stewards, CLC: Climate concerned, TEC: Technocrats, RES:
 Reserved) * SHA256:
 `5d540edcb39c8d7a14db315b5eaeed83689021ec43cbe16a1c7eb4467c943098` *
 `UserTweetIDs.txt` contains one tweet ID per user of the 1+ million users
 in our sample. These tweet IDs can be "hydrated" and used to
 find the users sampled in our study. * TweetID: single column listing one
 tweet ID per user * SHA256:
 `fbe1da240a5ab9d9aebac0aabbde247e6eeebfa77c5471cdb6136f45110b1111` *
 `EnvironmentalPundits.tsv` contains the user names and IDs for the
 environmental pundits whose timelines were used as the data source to
 train the probabilistic latent Dirichlet allocation topic model. *
 Columns: * Screenname: User name (e.g. GretaThunberg, which you can use to
 navigate to twitter.com/GretaThunberg) * ID: User ID * SHA256:
 `a6a987d934dea75e8ba2329820d6cfe354af0991f2bdbd4746b0f83ad6dafaa3` *
 `Persona_PoliticalIdeology.tsv` provides the mean political ideology score
 for the six personas * Columns: * mean: mean political ideology score *
 SEM: standard error of the mean * Persona: abbreviated name for the six
 personas * SHA256:
 `e568d9737cbd7c0b1b1ce61a6c9c8294f14a62d934446cc0d618ebf091bf1a13` *
 `US_geography.tsv` shows the state-level ranks for each persona * Columns:
 * name: State name * Persona: abbreviated name for the six personas *
 Rank: Rank for the 50 states (+ Washington DC) * SHA256:
 `8ba7e0ca437639656e25a473c4aec281e828e59941af847d8865bf4eddf1371d` ###
 Code * `Scraper.py` provides code that can be used to obtain user
 information from the `UserTweetIDs.txt` data file above to reproduce the
 user set in our analysis. * `Plotting.R` provides code to reproduce the
 plots in the main text.