  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.
