Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging

Tunazzina Islam, Ruqi Zhang, Dan Goldwasser
5/6/2023
cs.CLcs.AIcs.CY

No official code found. Best implementation: tunazislam/BMA-FB-ad-Climate (0 stars, last commit 2025-04-04).

Abstract

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.

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Code Implementations(1)

tunazislam/BMA-FB-ad-ClimateCommunity100%

AIES 2023

00PythonMay 6, 20231 years ago

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Cite this paper

@article{islam2023analysis,
  title  = {Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging},
  author = {Tunazzina Islam and Ruqi Zhang and Dan Goldwasser},
  year   = {2023},
  eprint = {2305.06174},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.06174v2}
}

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