# Understanding Opinions Towards Climate Change on Social Media

arXiv: https://arxiv.org/abs/2312.01217 · HTML: https://mlsanity.com/paper/2312.01217/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs](https://mlsanity.com/paper/2203.14883/) (2022)
- [Affective Polarization in Online Climate Change Discourse on Twitter](https://mlsanity.com/paper/2008.13051/) (2020)
- [Detecting Stable Communities in Link Streams at Multiple Temporal Scales](https://mlsanity.com/paper/1907.10453/) (2019)
- [Polarization of Climate Politics Results from Partisan Sorting: Evidence from Finnish Twittersphere](https://mlsanity.com/paper/2007.02706/) (2020)
- [Network Communities of Dynamical Influence](https://mlsanity.com/paper/1908.10129/) (2019)

## Abstract (arXiv)

Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the spreading of misinformation. In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media. To this end, we extracted and analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006 to 2019. Then, we construct a temporal graph from the user-user mentions network and utilize the Louvain community detection algorithm to analyze the changes in community structure around Conference of the Parties on Climate Change~(COP) events. Next, we also apply tools from the Natural Language Processing literature to perform sentiment analysis and topic modeling on the tweets. Our work acts as a first step towards understanding the evolution of pro-climate change communities around COP events. Answering these questions helps us understand how to raise people's awareness towards climate change thus hopefully calling on more individuals to join the collaborative effort in slowing down climate change.