Over the past decade, the analysis of discourses on climate change (CC) has gained increased interest within the social sciences and the NLP community. Textual resources are crucial for understanding how narratives about this phenomenon are crafted and delivered. However, there still is a scarcity of datasets that cover CC in news media in a representative way. This paper presents a CC-specific subcorpus extracted from the 1.8 million New York Times Annotated Corpus, marking the first CC analysis on this data. The subcorpus was created by combining different methods for text selection to ensure representativeness and reliability, which is further validated using ClimateBERT. To provide initial insights into the CC subcorpus, we discuss the results of a topic modeling experiment (LDA). These show the diversity of contexts in which CC is discussed in news media over time, which is relevant for various downstream tasks.

The debate around climate change (CC)—its extent, its causes, and the necessary responses—is intense and of global importance. Yet, in the natural language processing (NLP) community, this domain has so far received little attention. In contrast, it is of enormous prominence in various social science disciplines, and some of that work follows the ”text-as-data” paradigm, seeking to employ quantitative methods for analyzing large amounts of CC-related text. Other research is qualitative in nature and studies details, nuances, actors, and motivations within CC discourses. Coming from both NLP and Political Science, and reviewing key works in both disciplines, we discuss how social science approaches to CC debates can inform advances in text-mining/NLP, and how, in return, NLP can support policy-makers and activists in making sense of large-scale and complex CC discourses across multiple genres, channels, topics, and communities. This is paramount for their ability to make rapid and meaningful impact on the discourse, and for shaping the necessary policy change.