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.

通过社交媒体平台，研究了真实世界事件对社交媒体上个体对气候变化相关主题的观点的影响，包括社群检测、情感分析和话题建模等方面的研究。

了解社交媒体对气候变化的观点

Understanding Opinions Towards Climate Change on Social Media

The evolution and development of events have their own basic principles,
which make events happen sequentially. Therefore, the discovery of such
evolutionary patterns among events are of great value for event prediction,
decision-making and scenario design of dialog systems. However, conventional
knowledge graph mainly focuses on the entities and their relations, which
neglects the real world events. In this paper, we present a novel type of
knowledge base - Event Logic Graph (ELG), which can reveal evolutionary
patterns and development logics of real world events. Specifically, ELG is a
directed cyclic graph, whose nodes are events, and edges stand for the
sequential, causal, conditional or hypernym-hyponym (is-a) relations between
events. We constructed two domain ELG: financial domain ELG, which consists of
more than 1.5 million of event nodes and more than 1.8 million of directed
edges, and travel domain ELG, which consists of about 30 thousand of event
nodes and more than 234 thousand of directed edges. Experimental results show
that ELG is effective for the task of script event prediction.

本文提出了一种新型的知识库 - 事件逻辑图，ELG 将揭示真实事件的演化模式和发展逻辑，并对剧本事件预测任务具有有效性。