Generative Debunking of Climate Misinformation (2024)

Abstract: Misinformation about climate change causes numerous negative impacts, necessitating corrective responses. Psychological research has offered various strategies for reducing the influence of climate misinformation, such as the fact-myth-fallacy-fact-structure. However, practically implementing corrective interventions at scale represents a challenge. Automatic detection and correction of misinformation offers a solution to the misinformation problem. This study documents the development of large language models that accept as input a climate myth and produce a debunking that adheres to the fact-myth-fallacy-fact (“truth sandwich”) structure, by incorporating contrarian claim classification and fallacy detection into an LLM prompting framework. We combine open (Mixtral, Palm2) and proprietary (GPT-4) LLMs with prompting strategies of varying complexity. Experiments reveal promising performance of GPT-4 and Mixtral if combined with structured prompts. We identify specific challenges of debunking generation and human evaluation, and map out avenues for future work. We release a dataset of high-quality truth-sandwich debunkings, source code and a demo of the debunking system.

## introduction misinformation about climate change causes numerous negative impacts such as reducing climate literacy @xcite , polarizing the public @xcite , and cancelling out attempts to communicate factual information @xcite . psychological research has offered various strategies for reducing the influence of climate misinformation, including fact while increased co2 levels can enhance plant growth, they also have negative impacts on ecosystems, such as promoting the spread of invasive species and increasing the severity of plant diseases. moreover, elevated co2 levels contribute to climate change, leading to record high temperatures, ocean heat, and sea level rise. myth green plants require co2 for growth, similar to how humans need water and food. plants thrive at higher co2 levels, which existed in the past and are reproduced in some greenhouses, resulting in improved growth and yields. fallacy this argument oversimplifies the complex relationship between co2 and plant growth, ignoring the negative impacts of increased co2 levels on ecosystems and the overall climate system. while it's true that co2 is essential for plant growth, artificially elevating its levels in greenhouses does not account for the broader consequences of climate change, such as heat stress, drought, and extreme weather events, which have negative impacts on agriculture and ecosystems. fact although higher co2 levels boost plant growth, they also facilitate invasive species and disease spread, and exacerbate climate change effects, including temperature, ocean heat, and sea level rise. figure 1 : an example input myth (top, dark gray) and fact-myth-fallacy-fact ("truth sandwich") debunking generated by our model (bottom). the recommended approach of adhering to the factmyth-fallacy-fact structure of a debunking (figure 1 ; @xcite ). however, while psychological research provides best-practices for debunking, practically neutralising misinformation in real-world conditions is challenging. false information spreads faster and deeper than factual information on social media, making it difficult to counter misinformation before it has already done damage @xcite . in order to be effective, corrective interventions need to be deployed at scale and faster than misinformation can spread. automatic detection and correction of misinformation, a goal described as the "holy grail of fact-checking" @xcite , offers a solution to this challenge. 1 this paper presents efforts towards the completion of this "holy grail" by synthesising generative ai with past research on climate contrarian claim classification and fallacy detection, in an approach we call generative debunking. this approach adopts elements of the 4d framework @xcite which involves detecting, deconstructing, debunking, and deploying corrective interventions. specifically, we build upon the cards (computer assisted recognition of denial & skepticism) classifier which was developed to detect specific contrarian claims about climate change @xcite , and the flicc model @xcite that detects fallacies in climate misinformation, such as fake experts, logical fallacies, impossible expectations, cherry picking, and conspiracy theories @xcite . specifically, we implement our generative debunking framework by testing the ability of three unique combinations of prompting strategies of varying complexity with large language models (llms) of different size (section 4) to produce a structured and psychologically grounded "truth sandwich" debunking for a myth (figure 1 , section 2). we evaluate the quality of the produced debunking (sections 5, 6), identifying a lack of factuality and relevancy as a critical shortcoming even with the latest llms. in section 7 we discuss challenges of generating valid debunkings and their evaluation, and opportunities for future research. on the question of whether a debunking should mention the myth that is being refuted, there has been some speculation that debunkings should avoid mentioning misinformation lest the retraction causes a counterproductive "backfire effect" where belief in the myth is inadvertently strengthened @xcite . however, researchers have found difficulty in replicating the backfire effect @xcite . rather, it is rec-ommended that a debunking should repeat the misinformation once, as one repetition of the myth is beneficial to belief updating @xcite . however, further repetition of the misinformation should be avoided, as it makes information appear true, a phenomenon known as the illusory truth effect @xcite . next, the logical or argumentative fallacies underlying the misinformation should be explained @xcite . explaining fallacies is powerful as they are not domain specific, empowering recipients to see the same fallacies in other topics @xcite . explaining fallacies has also been shown to be effective in reducing the influence of misinformation regardless of whether the correction comes before or after encountering misinformation @xcite . incorporating fallacy explanations is especially important for more nuanced forms of misinformation such as paltering or cherry picking, which can involve truthful statements that are nonetheless misleading @xcite . for fallacy detection, this research relies on the flicc framework that summarises the five techniques of science denial-fake experts, logical fallacies, impossible expectations, cherry picking, and conspiracy theories @xcite . in a debunking, the fact should be repeated again at the end. wrapping facts at the start and end of a correction is known as a "truth sandwich" @xcite . information that is presented first and last is usually remembered best due to the primacy and recency effects @xcite . the repetition also makes the fact more likely to be believed by recipients @xcite . we adopt the four-layer "truth sandwich" (figure 1 ) to structure the output of the models described below. ## data our automated debunking system leverages various public datasets and a novel dataset specifically curated to provide gold-standard examples of the type of debunkings we are aiming for. ## generative debunking we present our generative debunking approaches in order of increasing complexity, ranging from a single generic prompt (section 4.1), over a single prompt with myth-specific external information (section 4.2; figure 2 , left), to a structured approach that prompts individually for each layer in the debunking (section 4.3; figure 2 , right). we apply the simpler approaches with stronger llms and vice versa. the single generic prompt is a complex endto-end instruction for the underlying llm that implicitly requires the model to perform fallacy detection, careful formatting, and fact retrieval. while it is easy to implement, it is limited to the llm's internal knowledge and requires a powerful (and expensive) model to compensate for the prompt complexity and produce good-quality output. we test it with gpt4. the single prompt with myth-specific external information incorporates external information and guided examples into a single end-to-end prompt. it is still limited to the llm's internal knowledge for fact retrieval. it is augmented with an external model for reasoning tasks that is not financially expensive to run. we test this more expressive prompt with a smaller, open source llm (palm2). the structured prompt with myth-specific external information involves more complex code that, in exchange, simplifies the generative debunking task into simpler subtasks. it incorporates a tool for searching the internet for specifics about certain topics and is also paired with the external reasoning model mentioned above. this most expressive prompt is combined with mixtral. given the trade-off between llm complexity and prompt complexity, we anticipate similar results among all three approaches. a more systematic experiment that disentangles the effects of llm choice and prompting strategy is left for future work, noting that the general tendencies reported here are likely to persist, while the specifics of direct llm comparisons tend to be short-lived with the rapid development of the technology. ## evaluation we evaluate the three models on their debunking of 20 climate myths, which were taken from the test data set of the flicc model (i.e., instances that none of our models has ever been exposed to during training). we devise a structured validation approach where fact 1, fallacy, and fact 2 of the debunking are separately rated on a scale of 1 (major flaws), 2 (minor flaws) and 3 (excellent). 6 the rating criteria are based on the rubrics that were used for evaluating students' work during the massive open online course on climate debunking. the full evaluation instructions are included in appendix ( four authors of this paper, one of whom is an expert in climate misinformation, independently evaluated 60 debunkings (20 myths debunked by the three models). the annotators were blind to the model which generated a particular debunking. the inter-annotator agreement is shown in overall, across all model outputs, we observe poor agreement for facts (both with the expert, and between the non-expert annotators), and a substantially better agreement for fallacies, which highlights the difficulty of judging the correctness and relevancy of the facts used in debunking for people who are not experts in climate misinformation. among the models, the agreement for palm2 outputs is the lowest, failing to reach moderate agreement even for the easier task of fallacy classification. in particular, non-expert annotators demonstrated widely different and inconsistent behaviours in judging palm2 samples. on further examination, these samples tend to contain more generic information than those produced by other models, which probably caused some of the annotators grade them more positively as generally relevant and correct, while the others punished them for lack of specificity and direct relatedness (see the middle column in in other words, the non-expert annotators demonstrated a consistent behaviour which went against the expert's judgements. while we discuss these issues in more detail in section 7, here we note that this is probably explained by the tendency of mixtral's outputs to contain more specific (but not necessarily correct and relevant) facts, which can fool the non-expert annotators to make uniform judgements (left column in finally, the averaged scores of the non-expert annotators are systematically more optimistic than the assessment by the expert alone ( ## results we now compare the quality of the debunkings generated by the three models, as rated by the annotators. an example of such debunkings is shown in in overall, the human scores for the detected fallacies and their explanations are higher than for the two facts at the beginning and end of the debunking, even more so for the expert scores. this may be surprising as fallacy detection and explanation is a hard task, even for humans. it also reinforces the well-known fact that even state-ofthe-art llms hallucinate non-factual information, or remain overly generic in their output. 6 51 fact while the sun does influence earth's climate, it is not the primary cause of modern climate change. over the past 200 years, only about 0.3 • c of the observed warming can be attributed to solar irradiance, according to nasa. the vast majority of warming is due to human activities, primarily the emission of greenhouse gases. [non-expert score: 3 (3, 3, 3); expert score: 2] myth ___ myth ___ myth___ fallacy this argument commits the single cause fallacy by suggesting sunspots alone explain recent global warming, ignoring the overwhelming evidence of greenhouse gas emissions' impact on earth's climate. [non-expert score: 2.67 (2, 3, 3); expert score: 2] fallacy this argument ignores the evidence that the sun has been cooling over the past few decades of global warming. it also assumes that solar activity is the only one driver for global warming, which ignores other factors such as manmade greenhouse gases. [non-expert score: 1.67 (1, 2, 2); expert score: 3] fallacy this statement commits the single cause fallacy, attributing the overall rise in temperature over the past 200 years solely to sunspots. however, only about 0.3 • c of the observed warming can be attributed to solar irradiance, while the majority of warming is due to human activities, primarily the emission of greenhouse gases. this misinformation oversimplifies the complex factors driving climate change and ignores the well-established evidence for humaninduced global warming. ## discussion evaluation challenges. in sections 5 and 6 we identified challenges with the evaluation of debunking quality by lay people: the low agreement between non-experts and with the expert; the tendency of lay people to score the outputs higher than the expert; and their tendency to over-rely on specific facts presented in the debunking. contrary to our expectations, these problems mostly concern evaluating the facts, rather than fallacies. this is probably because for fallacy evaluation the annotators were supported by the well-structured flicc taxonomy, while when assessing facts they had to rely purely on their own background knowledge and reasoning abilities. in particular, to correctly determine the quality of a fact used in a debunking, an annotator needed to first assess its correctness, and then evaluate its relevance to the specific myth being debunked, i.e. to decide if the fact effectively addresses the point made in the myth. thus, there are two significant challenges which non-experts face when evaluating the quality of facts in the debunking. first, they need to possess sufficient climate literacy to assess factual statements. for example, in response to the climate myth "incorrect ice age predictions in the 1970s discredit climate science", the debunking claim "there were no legitimate scientific predictions of a coming ice age in the 1970s" is factually incorrect. there were a handful of legitimate studies published in the 1970s predicting a possible ice age under certain conditions @xcite . a high level of climate literacy is required to make such a judgement, and in this particular considering the propensity of llms to generate fluent and seemingly plausible, but incorrect facts, this presents a major challenge for evaluating climate misinformation debunkings. second, it seems to be even more of a challenge to evaluate the relevancy of the suggested fact to the myth. for example, a mixtral-generated debunking in response to a myth which raises doubt about the reliability of climate models ("climate change is affected by innumerable interacting variables, atmospheric co2 levels being just one"), included the following fact: "yes, the increase in atmospheric co2 levels is a significant factor in climate change, as it traps heat and raises global temperatures, according to nasa and noaa." this statement is factually correct and topically related to the myth, so the non-expert annotators assigned it the highest score. however, the fact does not address the main point of the myth, i.e. the complexity of the issue and thus the inability of the climate models to predict the future, which is why the expert judged the fact as irrelevant and gave it the lowest score. as in the example above, the annotators may tend to consider a fact to be relevant if it is specific and convincing, overlooking the missing logical connection to the myth. on the other hand, if the fact is too generic, non-expert annotators tend to vary significantly in their judgments. for example, when judging the palm2 output "climate change is the long-term trend of rising global temperatures, and it is caused by human activities such as burning fossil fuels." which was used to debunk the myth "climate change is a hoax and has been rebranded multiple times to keep the fear mongering going", some non-experts said that the fact is irrelevant, while others considered it to be simple and to the point. our results and the examples above underscore the difficulty of the evaluation task, and suggest that it requires a direct involvement of a domain expert or at least their close supervision. moreover, they highlight the importance of high reliability of the system generating the debunkings, as a nonexpert user is unlikely to detect its flaws. ## conclusion climate misinformation has caused severe harms in the past, and its scale and effect are expected to increase with the rise strong generative ai, rendering manual debunking infeasible. introducing the framework of generative debunking, this paper incorporates psychologically grounded debunking methodology into large language models. we developed a series of prompting strategies tested with various llms, and validated manually by expert and non-expert annotators. our results point to major challenges in automatic debunking and concrete directions for future work, including an improvement of generated facts in specificity and relevance as well as the challenge of validating debunking systems with non-expert annotators. we hope that our code, data sets, and findings will initiate followup work to advance this promising line of work.