What drives climate action in diets, mobility and homes?
This dashboard enables exploration of climate discourse on Reddit - a social media platform - between 2010 and 2024. It focuses on the attitudes towards climate action in three consumption domains, that is, food, mobility and residential energy. It displays an LLM-supported analysis of the drivers and barriers of adoption of low-carbon behaviors. · For more information on data and methodology please see our methodology webpage or contact us at chowdhary@iiasa.ac.at / eker@iiasa.ac.at.
Data source
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The direct LLM view shows the original labels on the smaller review sample. The ModernBERT view shows the same questions after those labels have been used to scale the analysis across the much larger Reddit corpus. LLM labels come from the larger direct model and are slower, but more context aware. We use them to create the training data. SLM labels come from ModernBERT after it is trained on those LLM labels. We use this scaling step because the full Reddit dataset is too large to label directly with the larger model every time. The SLM is much faster on the large corpus, while the LLM view lets readers inspect the direct labels themselves.
Does the comment reference a social norm?
Each comment is first screened for norm-relevant discourse. This is broader than a narrow textbook norm detector: it includes explicit descriptive or injunctive norms, but can also capture Reddit-style public normative talk.
The plot shows the share of sampled comments in each sector that are coded as norm-relevant discourse by the currently selected data source (LLM or SLM). Food and diets emerges as the most norm-saturated sector, indicating that online discussion of dietary behaviour is disproportionately structured around social norms.
Count = comments mentioning that social group · % = share of all cross-sector group mentions in the filtered norm-bearing subset · Percentages may not sum to 100%; comments can mention multiple groups
The IPCC stresses that climate mitigation depends not only on technologies, but also on social and cultural processes that shape what actions people take and how change spreads through society. This panel complements the survey-style motivators and barriers by showing whether lifestyle choices are discussed in relation to broad public audiences or more personal social ties.
The plot shows which social groups are mentioned inside norm-bearing comments, after filtering to comments with descriptive or injunctive norm present. Food and diets contains relatively more interpersonal reference-group language than transport or homes/solar PV, suggesting that dietary behaviour is discussed in relation to closer social ties rather than the general public alone.
Descriptive or Injunctive Norm
Descriptive norm means language about what people do. Injunctive norm means language about what people should do, including approval, disapproval, praise, blame, ridicule, or prescriptive force.
The plot shows how often descriptive and injunctive labels are present or absent in each sector for the currently selected label source.
This panel helps separate talk about observed behavior from talk about approval or social pressure. Area = comment count in the current generated social-norm sample.
The sectors show broadly similar levels of descriptive norm presence, but Food/Diets stands out on Reddit for much stronger injunctive-norm language than Transport/EVs or Homes/solar PV.
Descriptive
Injunctive
Norms Over Time
The plot shows the yearly share of comments where the selected norm dimension is present for the currently selected label source. Each line is one sector.
Injunctive norms are consistently higher in the Food/Diets sector on Reddit, and their presence increased over time, from roughly 40% in 2010 to nearly 60% in 2024.
Keyword frequency in the full Reddit corpus per sector. Word size is proportional to log(count).
Corpus keywords
% of Reddit comments per attitude or barrier for the top 5 survey questions in this domain, ranked by mean prevalence in the later 2010–2024 scaled survey series.
% association
Filtering for lifestyle-specific Reddit discussions
FOOD / DIETS
TRANSPORT / EVs
HOMES / SOLAR PV
We first need a way to isolate the parts of Reddit discourse that are actually about a specific climate-relevant domain. These are search words and regex cues used to filter out irrelevant Reddit noise and isolate sector-specific discussion that is actually about food, transport, or home solar. Word size shows how often those terms appear in the selected corpus.
corpus keyword frequency
Prominence of factors affecting adoption of specific lifestyles on Reddit
AI can help us detect lifestyle choices and the factors influencing those choices in social-media discourse, even when the data were never originally collected for that purpose. With modern language models, we can project large-scale text into subspaces defined by the questions we choose to ask, without need for human labeling. In our case, we track survey questions on Reddit using LLMs.
Here we show the percentage of Reddit comments in which a given attitude or barrier is expressed. These numbers do not measure individual preferences the way surveys do. Instead, they capture an orthogonal dimension: how prominent a factor becomes in collective attention when lifestyle choices are discussed in public.
% of comments showing factor
% of Reddit comments in which a given attitude or barrier is expressed. Top 5 by prominence and confidence.
% yes by year (2010–2024) for the top 5 survey questions per domain under the currently selected label source. Year-over-year trends are the intended use; §3.1 explains the smaller direct LLM checkpoint table separately.
% of comments showing association by year
CLIMATE DISCOURSE ON REDDIT - LOW-AI Dashboard
1. Introduction
Climate mitigation requires more than technological solutions—it also depends on behaviour change shaped by individual, social, and structural factors[1,2,12]. Collective behaviours and social organisation are part of everyday life, and feeling part of active collective action can render mitigation measures more efficient and pervasive[13]. Social and cultural processes play an important role in shaping what actions people take on climate mitigation, interacting with individual, structural, institutional, and economic drivers[14]. Just like infrastructure, social and cultural processes can lock societies into carbon-intensive patterns of service delivery. They also offer potential levers to change normative ideas and social practices in order to achieve extensive emissions cuts[13,14]. Here, behaviour is treated as a discrete action in a specific consumption domain, such as shifting to a plant-based diet or using an electric vehicle instead of an internal combustion engine vehicle[12]. Individual drivers include cognitive and psychological factors such as climate risk perception or self-efficacy. Factors such as comfort, status, identity, and agency are associated with many technologies and everyday social practices that deliver energy services, from driving a car to eating vegan food[15,16]. Social drivers include the social costs of adopting or not adopting a behaviour, especially social norms and their influence. Action on climate mitigation is influenced by our perception of what other people commonly do, think, or expect, known as social norms[2]. Second-order beliefs—perceptions of what others in the community believe—are particularly important for leveraging descriptive norms[6]. Structural drivers include the surrounding socioeconomic and technical context, such as income, technology cost, availability, and convenience. Symbolic motives are more important predictors of technology adoption than instrumental motives[17,18].
Together, Food/Diets, Transport/EVs, and Homes/solar PV account for a large share of household carbon footprints and a large share of the public choices that shape climate mitigation. The current dashboard focuses on these three domains because they correspond to three concrete low-carbon behaviours: plant-based diets in food, electric-vehicle adoption in mobility, and rooftop solar PV adoption in homes.They also differ in how people make sense of them: transport choices are strongly entangled with symbolic car use and status[15,16], food choices are tied to health, climate, and identity-linked reasoning[12], and household solar adoption depends not only on costs and convenience but also on trust in providers and institutions[18,19,20].
Conventional ways of measuring readiness for these behaviour changes—surveys, experiments, polls, and related instruments—are useful but costly, slow, and limited in temporal resolution[3,5,7,9]. Trust in organisations is also a key predictor of the take-up of novel energy services, particularly when financial incentives are high[18,19,20]. Social media can complement these approaches. Reddit is especially useful here because it contains large volumes of open-ended, relatively nuanced discussion about everyday choices, reasons, barriers, and public reactions. This dashboard therefore uses Reddit as a large-scale naturalistic record of climate-relevant lifestyle discussion, rather than relying on surveys alone.
The objective is to explore the drivers and barriers of low-carbon behaviours using open-ended Reddit posts. For individual and structural drivers, the dashboard compares Reddit discourse with existing survey questions and measures how often those survey ideas appear in posts. For social drivers, it examines social influence through descriptive norms, injunctive norms, and reference groups. Across both parts of the workflow, large language models are used and their reliability is checked through repeated labeling, cross-model checks, and manual review. The result is both a substantive picture of climate discourse on Reddit and a methodological demonstration of how large language models can be used to complement survey-based measurement.
1.1 Knowledge Gap
IPCC AR6 WGIII highlights a knowledge gap in understanding the dynamic interaction between individual, social, and structural drivers of change, and in particular asks how social media influences the development and impacts of narratives about low-carbon transitions[1]. More research is needed to assess the role played by social media platforms in influencing emerging narratives of climate change and low-carbon transition[21]. Traditional measures of societal readiness—public opinion polls, climate opinion maps, and related survey-style instruments can be slow, costly, or limited in temporal granularity[8,9,12]. Existing social-media studies also tend to compress discourse into broad sentiment categories[10], ignoring the richer norm taxonomy that behavioural science has shown to matter (descriptive vs. injunctive, reference group specificity, and the difference between strict norm statements and public normative performances in Reddit-style discourse). No prior study has tracked survey questions directly in social-media data at scale. This dashboard projects Reddit discourse into subspaces defined by established survey instruments, revealing how prominent each factor becomes in collective attention when lifestyle choices are discussed publicly—capturing an orthogonal dimension to individual preferences measured by surveys.
1.2 Contributions
This dashboard uses Reddit discourse to extend and complement past survey-based work on climate-related behaviour. It asks whether the very same questions from carefully crafted survey instruments can be used with AI, at scale, to extract meaningful insights from observed public discourse rather than only from self-reported responses. In that sense, the dashboard does not simply reproduce survey categories on a new platform; it tracks an orthogonal dimension of collective attention, namely which motivators, barriers, and social pressures are publicly evoked when people discuss food, transport, and home-energy choices online. At a broader level, narratives about climate mitigation circulate within and across societies, and enable people to imagine and make sense of the future through processes of interpretation, understanding, communication, and social interaction[17]. The analysis therefore uses AI to recover structured signals from messy social-media text while also tracing social norms—approval, disapproval, observed behaviour, and reference groups—in public conversation.
Sample
29 survey questions · 1,500 comments per question, oversampled up to 5,000 where the answer categories were highly imbalanced
Prompt format
E.g.: “Links reducing meat to health?”
Yes/No classification
Social norms (Social Influence)
Sample 24,000 unique comments from the survey-aligned sample
What is measured
Norm presence
Descriptive norm
Injunctive norm
Reference group
🤖(9-billion parameter model)
LLM labels the sample (creating high quality training data)
labeled training data on lifestyle survey questions and social influence detection
(~293M parameter model)
ModernBERT-base trained to scale labeling efficiently.
As shown in the schematic, we begin with the matched Reddit corpus and then split the analysis into two linked branches: survey questions and social norms. The first branch uses a large language model to extract insights related to established survey questions from Reddit discourse, extending past survey-based work on climate-related behaviour to a naturalistic record of public discussion. The second branch detects the presence of social norms in the same data. This dual approach addresses a gap highlighted by IPCC AR6 WGIII, which calls for better understanding of how social media influences narratives about low-carbon transitions and how individual, social, and structural drivers of change interact dynamically.
3.1 Classifying survey questions in the matched Reddit corpus
The survey branch begins by defining which published survey questions are being tracked and how the training sample is constructed. Table 1 documents the survey-frame questions and their source trace. The sampling block below reports the direct LLM checkpoint and the larger ModernBERT inference sample used in the current dashboard version.
We then test whether the survey-style labels are stable enough to use as training data. To do this, we run the LLM twice on the same comments with the same prompt and compare the two outputs. Table 2 shows how this robustness check, together with the observed YES-rate, separates rare questions, unreliable questions, and questions that are kept for scaling. Table 3 then shows what happens in the next step, where those LLM labels are used to train ModernBERT and to decide which questions are strong enough to foreground in the Lifestyle Adoption Factors plots.
3.2 Evaluating the ModernBERT social-norm model on held-out data
We then ask whether the same comment pool can support stable social-norm labels for norm presence, descriptive norms, injunctive norms, and reference groups. Here too, the LLM first creates the training labels and ModernBERT is trained on those labels in the next step. Table 4 shows the held-out model quality for these four social-norm targets and indicates which parts of the Social Influence tab are currently on stronger footing than others.
The current dashboard slice uses four active social-norm questions per comment. The schema table below makes those targets explicit before the model-quality results are shown.
Social Norms (current dashboard slice: 4 active social-norm questions per comment, all sectors):
ID
Question
Options
1.1_gate
Does the comment reference a social norm?
yes / no
1.2.1_descriptive
Descriptive norm present?
present / absent / unclear
1.2.2_injunctive
Injunctive norm present?
present / absent / unclear
1.3.1_reference_group
Which social group is referenced?
coworkers / family / friends / general public / identity group / local community / neighbors / online community / other / other reddit users / partner/spouse
The F1 scores across all four social-norm targets are generally strong, indicating that ModernBERT learns the LLM-derived labels well. However, descriptive norms appear harder for the small model to predict than injunctive norms, where performance is noticeably better.
3.3 Computing confidence in the 9B-generated labels using a stronger 27B model
We ask whether the direct LLM labels themselves are credible enough to support the later training and scaling steps. For this purpose, we run a stronger model, Qwen 3.6 27B, on a sampled review set drawn from the survey and social-norm labels. Table 5 reports that external confidence check. These are the same confidence values that are then reported back into the dashboard plots, so that the visible plot annotations reflect stronger-model agreement with the original 9B labels rather than small-model scaleability. This step does not replace the current pipeline, but it indicates where the existing labels are reliable and where caution is warranted. In principle, one could continue to select ever-larger models as judges, but this escalation has no natural stopping point. Instead, our aim is to demonstrate a practical approach in which stronger, more computationally expensive models serve as reference judges for smaller, faster, and cheaper models. This establishes a scalable hierarchical system that, in general, can extract climate insights from large-scale social media data without requiring human labelling.
The 27B model's confidence check confirms the pattern seen in section 3.2: descriptive norms are harder for even the larger 9B model to identify reliably, compared to injunctive norms. This may reflect that the definitions of descriptive norms are somewhat vaguer; observing what others do is a subtler signal than explicit approval or disapproval. Overall, social-norm labels show lower confidence than survey-question labels.
3.4 Comparing Surveys to Reddit: Prevalence of Key Frames in U.S. Public Opinion
Language models let us project large-scale social-media text into subspaces defined by survey-style questions, even though those data were never collected for that purpose. What emerges is not a measure of individual preferences. Rather, it reflects how visible a given factor becomes in public conversation when climate action is on the table, an orthogonal dimension to what structured surveys capture.
Reddit % is the share of comments in which an LLM classifier flagged the frame as present (YES/NO, direction-agnostic). Survey % combines "strongly" + "somewhat" agree (Pew) or "very" + "somewhat" willing/positive (Gallup; Yale), The two measures capture related but distinct constructs and are not directly comparable.
The survey data show that respondents broadly agree all these factors influence their choices, with comparable agreement rates across questions. Reddit discourse tells a different story: it naturally differentiates between more commonly discussed frames, such as animal welfare in food choice, and those that receive far less public attention.
3.5 Limitations and Future Directions
As the reader may have noticed, the LLM tracks easier versions of the original survey questions in some instances. This is deliberate, as we found in our experiments that the complexity of a question can stump an LLM of a certain size; a larger model may resolve this at the cost of more compute and parameters.
This limitation has another cause, separate from question complexity. The Reddit data are not self-reported descriptions of individuals; they are better understood as projecting large-scale text into subspaces defined by the questions we choose to ask. There is no guarantee that Reddit discourse supports such projections for arbitrary survey questions.
In future work, as models improve in capability and efficiency, the same methodology could be applied to track arbitrary climate-change-related questions across large-scale textual or video data. This would extend the dashboard's approach beyond Reddit to other platforms and modalities, enabling broader monitoring of public discourse on low-carbon transitions.
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