The Climate Change Debate and Natural Language Processing (2021)

Abstract: 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.

## introduction anthropogenic climate change (cc) has become a central topic of global, national, and local debates across multiple arenas and channels that involve virtually all branches of society. from private talk to public social media exchanges, from scientific papers to journalistic articles in traditional mass media, from statements by stakeholders (industry, civil society groups, etc.) to political deliberations in national parliaments or in international organizations-no sphere is without references to climate change. while climate scientists have reached a consensus that climate change is real, that it is caused by human activity on the planet, and that is has and will have adverse effects for humanity and the biosphere around the planet @xcite , public debates on cc and on the policy implications remain highly controversial (see, e.g., @xcite ). natural language processing (nlp) is wellpositioned to help study the dynamics of the largescale and complex discourse on cc. activists and policy-makers need nlp tools through which they can filter, order, and make sense of the vast amount of textual data produced on cc. however, within the nlp community, the amount of work done so far on cc remains limited. in the words of @xcite , the topic of climate change "has received little attention in nlp despite its real world urgency". this is in contrast to the attention that cc discourses receive in climate and environmental science and in various social sciences. we argue in this paper that the research questions, insights and methods applied in these disciplines can provide useful orientation for nlp practitioners. and conversely, the general advances in nlp can provide more reliable and valid tools to actors aiming at shaping policy and influencing individual behavior. such tools for monitoring the discourses across the multitude of channels, genres, speakers, and topics can enable policy-makers and activists to more rapidly respond to discourse shifts, which is of huge importance given the speed of the ongoing climate change. to set the stage, in section 2, we explain what we mean by cc "discourses" and we delineate the different readings of the term. next, section 3 takes the viewpoint of the nlp community and summarizes work that has been done in the field so far. section 4 describes key studies taken from the social science literature, which study cc discourse in different ways and to different ends. our emphasis here is on the methodological choices that are being made. section 5 provides a comparative analysis and proposes points of synergy that we regard as recommendations for nlp work. our conclusions on the potential positive impact of nlp for making sense of the cc debate are presented in section 6. ## climate change "discourses" the term discourse is both polysemous and vague. in nlp and its branch of 'discourse processing', its default reading refers to a single text or a single dialogical interaction that becomes an object of study, involving phenomena that cross sentence boundaries (anaphoric reference, coherence relations, and so on). that reading is largely irrelevant for our purposes here. in the social sciences, theories and definitions of discourse(s) and methods of discourse analysis are highly diverse. in the context of environmental policy, @xcite 175) define a discourse as the "ensemble of ideas, concepts and categories through which meaning is given to social and physical phenomena, and which is produced and reproduced through an identifiable set of practices". thus, when we refer to the climate change discourse, we refer to the ensemble of practices of writing about or debating cc-related matters by one or multiple actors in various physical or digital arenas. in much of the empirical literature on cc debates that we review below, this results in a focus on one of two dimensions of discourse: once one zooms in on the stances on cc more closely, further dimensions of discourse 2 become visible. for example, @xcite develop a more fine-grained stance classification distinguishing between "industrial fatalism", "green keynesian", "eco-socialist" and "climatesceptic" discourses. 1 whether studies on cc detect a divided debate or a relatively unified conversation @xcite will depend on the types of discourse dimensions studied as well as on the level of analysis. this should be important also for nlp practitioners when they select a set of data for their work, as certain differences in nuances on stances may remain inconsequential in a social media debate between individuals, but can have significant policy implications when uttered by political leaders in a parliamentary debate. ## cc discourse: research in the nlp community the difference between this and the following section is one of scientific community: in the present section, we briefly summarize work that has been done on cc-related data and was presented at nlp/computational linguistics or ai meetings. the number of such publications is small, so we mention them here in chronological order. henceforth, we use lowercased "cc" and "gw" as shorthand for "climate change" and "global warming", respectively, as a search bigram employed by researchers for retrieving their data. @xcite crawled 1.5 mio posts from 3,000 blogs, found by the query term cc or one from a short list of other terms, and manually coded a selection of blogs as belonging to sceptic or accepter discourse. 133 topical terms of cc discourse are taken from previous work, and for each term, correlations with "virtue" and "vice" words (from the general inquirer lexicon) are computed for both groups of blogs. then visual analytics are applied to manually compare the discourses. differences between blogs are found to be mainly in the framing of "climate science" and "quality of life". in continuation of this work, @xcite built a corpus of cc blog posts in three languages. they applied network analysis to the graph of blog linkages and detected four prominent communities of bloggers. the cc topic became more visible in the nlp community when @xcite introduced the new semeval task "stance detection of tweets", where "climate change is a real concern" was one of five statements for which a dataset was built. beyond this, however, cc was not addressed in any more specific way. @xcite collected tweets around the 2015 un cc conference in paris, using about 20 search keywords and a similar number of hashtags, as well as three twitter accounts dedicated to the conference. term lists for cc subtopics are constructed by extending seed words with similar words gathered by a word2vec model. then, opinion and emotion analysis tools are applied. results are plotted in particular for correlations of emotions and topics and the role of "influencers" versus less prominent accounts. @xcite gathered 11,000 newspaper articles from four british broadsheets over the years 2007-2016. the search criterion was that cc has to occur at least three times. they use lda to find sentiment targets in the texts, and by employing sentiwordnet to label keywords in the associated topics, they found some differences between newspapers in their topic-sentiment association. recently, @xcite were the first to apply a broad range of current nlp techniques to the cc domain. they introduce a corpus of 2,000 cc sentences from 63 us news sources (2000-2020), which were labeled by crowdworkers for stance toward "climate change is a real concern" (cf. @xcite above). the base corpus of 56,000 articles was built with four bigram and two unigram query terms. dependency parsing and coreference resolution are applied to enable extraction of opinion statements using a set of hand-coded patterns. these statements allow to distinguish self-affirming versus opponent-doubting frames in quoting sources of information. a bert model is employed for stance classification, allow-ing to identify accepter and sceptic media. recently, koenecke and feliu-fabà (2020) study whether cc sentiment in tweets changed in response to five natural disasters occurring in the us in 2018. tweets had to contain one of the terms cc or gw, plus at least one instance of a set of natural-disaster terms. this yielded 800 pre-event and 6,000 post-event tweets. an array of standard ml tools were tested for classifying accepter versus sceptic tweets. rnns with glove embeddings performed best, yielding an accuracy of 75%. a cohort-level analysis then shows that the 2018 hurricanes yielded a statistically significant increase in average tweet sentiment affirming cc, while other disasters did not. ## cc discourse: research in the social sciences in the following we provide a synthesis of a subjective selection of papers from journals in communication science, political science, and climate/environmental science that address cc discourse. all selected contributions take a "text-asdata" approach @xcite and use either semi-automatic methods such as corpuslinguistic collocation analysis or fully-automatic text mining methods. the papers we chose are either frequently cited or representative for widespread methodological approaches; a few are selected because they are innovative, either in terms of method or in terms of the text genre(s) being addressed. we group the discussion along the targeted text genres or media (i.e., our discourse 1 dimension), to illustrate the range of underlying social science research questions and the data used to answer them. then, in the second subsection, we summarize and assess the methods used, and we close the section with remarks on the relation between qualitative and quantitative research. ## analyzing the cc debate: goals and methods in the social sciences, three criteria are often used to assess the quality of research (see, e.g., @xcite ): the first point corresponds quite clearly to the goal of reproducibility in nlp and does not require further comment here. in this section, we will thus reflect on the other two points. an the end, we summarize the takeaway messages that we propose for nlp. ## conclusions: climate change, nlp, and the impact for social good in this contribution, we have argued that nlp and social science can enrich each other to more comprehensively study the complex discourse(s) on climate change across channels, genres, communities, and topics. this is important because the cc debate is unfolding among three large and diverse actor communities: each community uses different genres, registers, and terminologies to communicate with each other and with other communities about cc. these communities shape individual and collective ideas, frames, and, ultimately, the behavior that is consequential for the future evolution of anthropogenic climate change. while social scientists explore this complex discourse in qualitative and quantitative research, they lack the full toolbox to do so at scale. and while nlp researchers are continuously expanding the general nlp toolbox, they have so far been selective in the channels and questions they focus on when it comes to cc, more or less choosing "the usual suspects". the positive impact of combining both perspectives is not guaranteed, but possible. as societies increase their ability of "making sense" of the cc discourse, they get better at understanding and evaluating the politics and discourse landscape: who is trying to frame cc discussions, on what channel, in what way, and for what interests? is the cc debate polarized, controversial, fragmented into echo chambers or simply nuanced in an attempt to find socially and politically accepted solutions? which frames are intentionally placed, and which are taken over, consciously and subconsciously, in traditional and new media? why are some frames more successful and thus more likely to shape ideas that define public policy or collective behavior in relation to cc? where nlp can help answer these questions in reliable/reproducible, representative, and valid ways, it can have a positive impact for the social good beyond enriching the social sciences: ultimately, it may provide each of the three communities mentioned above with the ability to judge in what direction one of the most important debates of our time-the climate change discourse-is evolving, and to respond accordingly.