# Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging

arXiv: https://arxiv.org/abs/2305.06174 · HTML: https://mlsanity.com/paper/2305.06174/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 62 references that are in the map.
- First posted on arXiv 6 May 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://mlsanity.com/paper/2006.03659/) (2020)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [CLEAR: Contrastive Learning for Sentence Representation](https://mlsanity.com/paper/2012.15466/) (2020)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models](https://mlsanity.com/paper/1902.10547/) (2019)
- [Pre-trained Language Model Representations for Language Generation](https://mlsanity.com/paper/1903.09722/) (2019)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention](https://mlsanity.com/paper/1801.03032/) (2018)
- [On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook](https://mlsanity.com/paper/1808.09218/) (2018)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy](https://mlsanity.com/paper/2103.09224/) (2021)
- [Weakly Supervised Learning for Analyzing Political Campaigns on Facebook](https://mlsanity.com/paper/2210.10669/) (2022)
- [Facebook Ads: Politics of Migration in Italy](https://mlsanity.com/paper/2010.04458/) (2020)
- [Twitter User Representation Using Weakly Supervised Graph Embedding](https://mlsanity.com/paper/2108.08988/) (2021)
- [Learning Twitter User Sentiments on Climate Change with Limited Labeled Data](https://mlsanity.com/paper/1904.07342/) (2019)

## Built on by (most cited first, 3 of 3)

- [Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy](https://mlsanity.com/paper/2404.10259/) (2024)
- [Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media](https://mlsanity.com/paper/2502.01991/) (2025)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)

## Abstract (arXiv)

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.