No AI on a Dead Planet: Sentiment and Emotion Analysis Across R eddit Communities on AI and the Environment (2025)

Abstract: This paper investigates how different online communities perceive and discuss the environmental impact of AI through sentiment analysis and emotion detection. We analyze Reddit discussion from r/artificial and r/climatechange, using pre-trained models fine-tuned on social media data. Our analysis reveals distinct patterns in how these communities engage with AI’s environmental implications: the AI community demonstrates a shift from predominantly neutral and positive sentiment in posts to more balanced perspectives in comments, while the climate community maintains a more critical stance throughout discussions. The findings contribute to our understanding of how different communities conceptualize and respond to the environmental challenges of AI development.

## introduction the debate on the impact of artificial intelligence (ai) is multifaceted, encompassing different areas of society and, more broadly, environmental sustainability @xcite . one of the most pressing issues is the ecological footprint of ai systems, primarily due to their intensive computational requirements and consequent energy consumption of increasingly larger models @xcite . the extensive training of these models leads to substantial co2 emissions and water consumption, with projections suggesting large language models (llms) could potentially reach over 30% of the world's total energy consumption by 2030 @xcite . the environmental cost extends beyond training to daily usage. the public release of chatgpt, powered by gpt-3, sparked widespread adoption of ai assistants, leading to a dramatic increase in their collective energy usage. for instance, each chatgpt query during inference consumes energy equivalent to running a 5-watt led bulb for 1 hour and 20 minutes. furthermore, the carbon footprint of these systems is intertwined with broader issues of extractivism, both material and immaterial. this includes the resource mining, energy consumption, and product obsolescence cycles required to manufacture the hardware and infrastructure supporting ai @xcite . these are all issues that must be addressed within the nlp scholar community, as our research work relies more heavily on llms. several efforts have been devoted to understanding and reducing the environmental impact of ai techniques @xcite . for example, researchers have developed a carbon emission tracking tool for models training process @xcite , a lighter version of existing models @xcite and they created optimized techniques for a better efficiencyconsumption trade off like the gaussian processbased bayesian optimization @xcite . despite growing evidence of ai's environmental impact, studies have shown that these technologies are often perceived as more sustainable, or less environmentally harmful than they actually are @xcite . building on previous research @xcite , this paper investigates the online discourse surrounding ai's environmental impact through sentiment analysis (sa) and emotion detection. in particular, we focus on two interest-based communities on reddit, the forum social network: the subreddits r/artificial and r/climatechange. by examining and comparing these two distinct threads of online conversation, this paper aims to shed light on the emotional gradient of online conversations on the matter. the paper is structured as follows. section 2 reviews existing studies on sa in climate changerelated discourse; section 3 presents our methodology and collected data, describes the models that were used to perform the analysis; section 4 discusses the results of the analysis; and section 5 outlines conclusions and future research directions. ## related work in recent years, the application of natural language processing (nlp) and sentiment analysis (sa) to analyze the discourse on climate change (cc) and related environmental matters increased. nlp methodologies were adopted for stance classification @xcite , entity recognition in environmental texts @xcite , bias detection in corporate communication @xcite and sustainability reports @xcite . moreover, a growing body of research has examined bias in environmental discourse. scholars like @xcite and takeshita et al. @xcite have explored anthropocentric and speciesist biases in language, and developed methods to address these issues. the framing of environmental topics in media and political arenas has also been studied, investigating how these frames shape public perceptions and influence policymaking @xcite . sentiment analysis in particular was adopted to analyze tweets corpora to capture the broad public feeling on climate change @xcite . similarly, existing work focuses on public opinion or emotional responses towards particular ecological events or phenomena @xcite . despite the growing environmental implications of ai systems, the field of natural language processing has insufficiently explored how different communities perceive and discuss these ecological impacts. this study addresses this research gap through a comparative analysis of sentiment and emotional patterns in discussions across aifocused and climate-focused online communities, offering insights into how distinct epistemic communities conceptualize the environmental implications of ai technologies. ## results and discussion the sentiment and emotion analysis reveals distinctive discourse patterns across two specialized communities under analysis. the analysis shows robust reliability with average confidence scores of 71% for sentiment classification and 85% for emotion classification across both datasets, with particularly high confidence in detecting the most frequent emotions. the findings are presented first through an individual corpus analysis and then through a comparative analysis supported by representative examples that highlights divergences in framing, response patterns, and community engagement dynamics. ## conclusion and future work this study presents an analysis of sentiment and emotion patterns in discussions about ai's environmental impact across two reddit communities. the ai community shows a neutral-positive sentiment, while the climate community is more neutral in posts but varies between negative and neutral in comments. emotionally, ai discussions feature approval, curiosity and admiration; the climate corpus reveals a slightly broader emotional spectrum, with higher frequencies of critical emotions like disapproval and annoyance. qualitative analysis reveals different problem-framing approaches. the ai community tends to approach environmental concerns as technical challenges amenable to optimization, often transforming warnings about energy consumption into discussions of efficiency improvements. the climate community's responses indicate attention to systemic environmental impacts, with a marked skepticism toward technological solutions. future work will address current limitations of this study through the development of a highquality, manually annotated dataset focused on the topic of our interest. the creation of a gold standard dataset will enable proper evaluation of different models' performances on our specific domain. to this end, we will develop annotation guidelines, conduct inter-annotator agreement studies, and create a corpus capturing the language patterns that are present in discussions about ai's environmental impact. such a resource would not only allow for more reliable model evaluation but could also serve as training data for fine-tuning models specifically for this domain. expanding the analysis to include more diverse online communities could also enrich the findings and reveal additional patterns. finally, the differences between ai and climate subreddits highlight the need to foster connections and encourage collaboration between commmunities. future research could create a more productive dialogue, perhaps through development of shared tools or frameworks that combine both approaches to assess and tackle ai's environmental impact.