Understanding Opinions Towards Climate Change on Social Media

TL;DR

This study analyzes 13.6 million tweets over 13 years to understand how real-world climate events influence public opinion and community structure on social media, using network analysis and NLP techniques.

Contribution

It introduces a comprehensive analysis of social media discussions on climate change around COP events, combining community detection, sentiment analysis, and topic modeling.

Findings

Community structures change around COP events.

Sentiment and topics vary with real-world climate developments.

Social media communities evolve in response to climate-related news.

Abstract

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.