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.

翻译：像Twitter（现称为X）这样的社交媒体平台彻底改变了公众参与重要社会和政治话题的方式。近年来，社交媒体上关于气候变化的讨论成为政治两极分化和错误信息传播的催化剂。在本研究中，我们旨在理解现实世界事件如何影响社交媒体上个体对气候变化相关话题的观点。为此，我们提取并分析了2006年至2019年间由360万用户发送的1360万条推文数据集。然后，我们构建了一个基于用户-用户提及网络的时间图，并利用Louvain社区检测算法分析了围绕气候变化缔约方会议（COP）事件的社区结构变化。接着，我们还应用自然语言处理领域的工具对推文进行情感分析和主题建模。我们的工作朝着理解COP事件周围支持气候变化社区的演变迈出了第一步。回答这些问题有助于我们理解如何提高人们对气候变化的意识，从而有望号召更多人加入减缓气候变化的合作努力中。