Illuminating Logical Fallacies with the CAMPFIRE Corpus (2025)

Abstract: Misinformation detection remains today a challenging task for both annotators and computer systems. While there are many known markers of misinformation—e.g., logical fallacies, propaganda techniques, and improper use of sources—labeling these markers in practice has been shown to produce low agreement as it requires annotators to make several subjective judgments and rely on their own knowledge, external to the text, which may vary between annotators. In this work, we address these challenges with a collection of linguistically-inspired litmus tests. We annotate a schema of 25 logical fallacies, each of which is defined with rigorous tests applied during annotation. Our annotation methodology results in a comparatively high IAA on this task: Cohen’s kappa in the range .69-.86. We release a corpus of 12 documents from various domains annotated with fallacy labels. Additionally, we experiment with a large language model baseline showing that the largest, most advanced models struggle on this challenging task, achieving an F1-score with our gold standard of .08 when excluding non-fallacious examples, compared to human performance of .59-.73. However, we find that prompting methodologies requiring the model to work through our litmus tests improves performance. Our work contributes a robust fallacy annotation schema and annotated corpus, which advance capabilities in this critical research area.

## introduction identifying and addressing misinformation remains a challenging, labor-intensive task today. particularly in situations that are fast-changing-such as natural or infrastructural disasters, disease outbreaks, military conflicts, and political crises-the spread of misinformation can easily outpace the available resources and human capital needed to address it. automatic and human-in-the-loop strategies show some potential to reduce the cost of labor figure 1 : we show a visualization of fallacies identified in text. although these are manual annotations shown, our corpus supports automatic markup of documents producing such a visualization for readers requiring automatic assessment of the credibility of a document, particularly in topic areas where fact-checking is not readily available. for identifying misinformation, but there remain challenges to algorithmically and robustly identifying misinformation in arbitrary text. we envision reliable tools that can facilitate the automatic markup of text with likely misinformation markers (see figure 1 ). to address these challenges, we developed the campfire (combined annotations of misinformation, propaganda, and fallacies identified robustly and explainably) corpus-a corpus of texts on various topics (covid-19, the russian invasion of ukraine, and the 2023 ohio train derailment) annotated with markers useful for identifying misinformation. although we divide these markers into testable and untestable beliefs, fallacies, and propaganda types, in this paper we narrow our focus to logical fallacy annotation. one advantage of focusing on logical fallacies as opposed to fact verification is that they allow us to scrutinize the soundness of a text's arguments in a content-neutral way, even if many of the facts involved are not yet known. we address weaknesses of previous annotation schemas for fallacies by developing rigorous linguistic tests-inspired by the notion of frames and frame elements @xcite for each annotation label so that they can be applied consistently and objectively across domains. we find that our annotation methodology reduces the subjectivity of fallacy annotation, resulting in relatively high inter-annotator agreement (iaa): our agreement on a triple-annotated dataset, as measured by cohen's κ, is in the range .69-.86 based on pairwise comparison of three annotators. technologies for identifying and addressing misinformation are particularly relevant today, given the popularity of generative, large language models (llms), the reliance of llms on online text, and the tendency of these systems to hallucinate. to establish baseline system performance on fallacy identification and recognition, we experiment with two of the largest, most advanced models (gpt-4o, gpt-o1) to predict campfire fallacy labels. performance leaves much to be desired: gpt-o1 achieves the best f1-score of .08 when excluding non-fallacious examples. although this demonstrates the continued challenge of this task, we find that providing the litmus tests used by our annotators improves model performance. after describing related work (section 2), we present our theoretical framework, based upon first identifying the relevant, valid reasoning types (section 3), followed by our annotation schema, including litmus tests ensuring diagnostic criteria for certain fallacy labels (section 4). we then describe our corpus and annotation procedures, concluding with resulting iaa measures demonstrating the clarity and robustness of our schema (section 5). we conduct experiments to establish baseline llm performance in recognizing fallacies across three evaluation documents (section 6). 1 our discussion compares the challenges of human and system performance on this task, and we propose that our litmus tests reduce subjectivity in this task (section 7). we conclude with suggestions for further system improvement on the critical task of fallacy and misinformation detection (section 8). 1 our corpus and full experimental results and prompts can be found here: https://github.com/melissatorgbi/ campfire ## related work there has been an surge of research in nlp on detecting misinformation and related tasks, including fake news detection and automatic factchecking, stance and sentiment analysis, and rumor detection, resulting in various workshops and shared tasks. thus, there are a variety of annotation schemas and datasets focused broadly on the detection and analysis of misinformation, which may have some overlapping categories with our research. these datasets include the semeval 2020 annotated dataset @xcite , and the credibility indicators outlined by @xcite . here, we survey related work supporting the areas of fact-checking, propaganda techniques, and fallacy detection. both fact-checking generally and fake news detection more specifically require comparing claims against some ground truth, widely accepted facts. @xcite focus on fake news detection that compares claims against knowledge graphs. instead of focusing on a document-level classification of fake news, @xcite cross-check individual elements of the document that better captures fake news where only a small portion of the document has been manipulated. one distinction between campfire and fake news detection research is our focus on misinformation markers that do not require outside knowledge or ground truth facts to compare against. our focus facilitates misinformation detection in subject-matter domains that are fast-changing, where the facts of a situation are not yet known or understood, such as the early weeks of the covid-19 pandemic. propaganda techniques facilitate the acceptance and spread of certain claims, often in lieu of credible evidence and argumentation. da san martino et al. (2020b) offer a survey of relevant work on propaganda detection. da san martino et al. ( 2019 ) developed a corpus annotated with 18 labels describing propaganda techniques in which the annotators chose both the label and the span of the annotation, obtaining a γ inter-annotator agreement of .53. recently, llms have been leveraged for propaganda detection. @xcite leverage gpt-3 and gpt-4 for classifying the propaganda techniques in the semeval 2020 task 11 dataset. 2 the best gpt-4 performance achieves an f1-score of 58%, while the state-of-the-art system, which uses a fine-tuned roberta model, achieves an f1-score of 63% @xcite . this demonstrates that the mere increase in scale of an llm does not guarantee superior performance on this challenging task. furthermore, the performance across the detection of particular techniques and fallacies varies wildly-loaded language (f1-score of 72%) and name calling (f1-score of 65%) set the upper bound, while repetition (22% f1-score), bandwagon, and reductio ad hitlerum (24% f1-score) sit on the lower bound. from this, we hypothesize that techniques with a clearer linguistic signature (as we would expect from loaded language and name call-ing) are much easier to detect. like propaganda techniques, logical fallacies make a claim that may appear persuasive but is not supported by credible evidence or a logically sound argument. the argotario corpus @xcite is one of the few corpora focused exclusively on logical fallacies, but their research crowd-sources annotations of just five logical fallacies. @xcite attempt to replicate the argotario annotation with expert annotators annotating logical fallacies in various publications, and show that the categories do not facilitate good iaa, nor can the distinctions be replicated by a system in a few-shot learning setting. in @xcite , potential fallacies are collected automatically from reddit by searching for mentions of fallacies in comments, and then these are filtered through crowdsourced judgments. here again, iaa is somewhat low, particularly for hasty generalization, where agreement was measured via cohen's κ at .38. this underscores the challenge of this annotation task. the authors explore several models for automatic prediction of fallacies, including bert and mgn, with resulting f1-scores between 13 and 42% on the task most comparable to ours of labeling a comment with a particular fallacy. unsurprisingly, given the correspondingly low iaa, the lowest f1-score is for hasty generalization. we apply several lessons learned from related work. first, our schema supplies rigorous and detailed litmus tests facilitating objective determination of each annotation category. second, the campfire schema is refined until achieving satisfactory iaa, as the systems trained on data marked campfire fallacies (e.g., band wagon and reductio ad hitlerum). up with categories with relatively low iaa demonstrate correspondingly poor performance on those categories. third, campfire annotations focus on misinformation markers that can be identified from linguistic or structural features of a text, rather than external knowledge, as this reduces ambiguity in the annotation process and makes our schema more applicable in fast-changing domains where the facts are not yet known. ## theoretical framework a fallacy is an error in reasoning, argument, or methodology that leads to an unsound inference. a fallacy may be intentional or unintentional. because fallacies are erroneous forms of inference, it is useful to categorize fallacies based on the type of inference they attempt to make. campfire's fallacy taxonomy groups fallacies based on five inference types: a testimony fallacy can involve relying on an uncredible or unqualified source, relying on testimony without identifying the source, or using the commonality of a belief as evidence that it is true. testimonial fallacies in campfire include: bandwagon, irrel-evant authority, sourceless testi-mony, ambiguous source, appeal to confidence/disbelief, plain folks. organizing the taxonomy this way also allows us to explain why techniques in each category are fallacious, because we can compare them to credible forms of inference and identify the differences. ## annotation schema we recognize three major challenging sources of ambiguity in the annotation of fallacies: these challenges inform the design of our annotation schema. we address them using a collection of strategies meant to reduce the annotators' burden to make subjective judgments. annotating clauses. the annotation anchor of each campfire fallacy label is always a clause. each clause is a span of tokens within a sentence. we use a preprocessing script to first identify clauses in a text before annoatating. this script parses text into universal dependency trees (de @xcite . dependencies that correspond to a clause (root, csubj, csubj:pass, ccomp, advcl, advcl:relcl, acl, acl:relcl, xcomp, parataxis) are used to select the token span under that subtree. we also include coordinated clauses (under conj) and-for the sake of identifying testimonial fallacies-prepositional phrases evoking a reporting events (e.g., 'according to . . . ') are also treated as "clauses" for purposes of annotation. this procedure produces a (possibly nested) list of text spans each with the potential to be an annotation anchor. this allows for more fine-grained annotation than annotation by sentence, but involves less subjectivity than asking annotators to choose an arbitrary span by hand. 3 because some fallacies can conceivably span over many clauses or sentences, each fallacy guideline also includes rules for identifying its conventional annotation anchor in order to further reduce this source of ambiguity. fallacy guidelines. in practice, identifying fallacies can be a very challenging task because ar-guments in the real world that invoke a fallacy do not all take the same structural form or rely on the same lexical items or linguistic markers. additionally, a real-world argument might have degrees of similarity to a known fallacy, in which case annotators might disagree about how similar it must be in order to deserve a fallacy label. to address this challenge, we develop rigorous annotation guidelines for each fallacy in our schema to drastically reduce this source of ambiguity. we start by observing that each fallacy has a logical form with premises and a conclusion. each fallacy also has 'frame elements,' concepts evoked by the fallacy that must be in a particular relationship with each other for the fallacy label to apply. figure 2 , for example, shows the guidelines for the slippery slope fallacy. text that is labeled as slippery slope must evoke frame elements: person/group a who initiates the events and events e and e' which are the starting and resulting events of the slippery slope. the advantage of relying on frame elements and other litmus tests is that annotators are asked whether they can identify concepts in the text corresponding to the correct frame elements and whether these elements meet particular criteria, greatly reducing the subjectivity of the task. during annotation, annotators consider a fallacy's logical form, frame elements, and tests to decide if that fallacy label can be applied. during adjudication, annotators again consult the guidelines to resolve disputes. although frame elements are not annotated explicitly, they provide a rigorous litmus test to identify fallacies as objectively as possible. limiting external knowledge. another major challenge in the design of this schema was the issue of reliance on external knowledge. early group annotations of fallacies revealed that often correctly identifying a fallacy in some text depended greatly on annotators' knowledge about the particular subject being discussed. annotators with different levels of expertise or different preconceptions tended to make different judgments, resulting in lower agreement. we decided early on to reduce this source of ambiguity by focusing on fallacies that could be identified without relying on external knowledge or relying on it as little as possible. for example, an early version of our schema included the label straw man which is a fallacy of relevance where an opponent's position is mischaracterized in order to make it seem weaker than ## corpus in this section, we present the corpus of our research into the detection of misinformation across a diverse range of documents. the corpus in total comprises fourteen documents sourced from a variety of publications, including scholarly works, tabloids, and major news organizations. our corpus distribution across topics is summarized in the process of document selection began with the selection of a range of medical documents on the topic covid-19 at the start of the pandemic. the topics of these papers spanned the safety in wearing masks, the effectiveness of herd immunity, vaccination safety, and long-term illnesses. as we've developed our misinformation guidelines, we've broadened our annotation work to include the international conflict of the russo-ukrainian war, and an ecological disaster, known as the ohio train derailment. ## experiments: llm baseline to establish baseline system performance on the task of recognizing and labeling fallacies, we use openai's gpt-4o-2024-08-06 (gpt-4o) and o1-2024-12-17 (gpt-o1). these models were selected as representative of current llm capabilities due to their large size. gpt-o1 was chosen alongside gpt-4o for its reported ability to handle complex reasoning which may be beneficial for this task. the temperature for gpt-4o and gpt-o1 were 0 and 1 respectively, which were the lowest options for each model to make the outputs more deterministic. three documents that had been triple annotated and adjudicated were selected for evaluation, thereby giving us a clear picture of how llm performance compares to manual annotation. a total of 22 tests were run, including experiments to investigate what information from the guidelines to include in the prompt. ## discussion our results show that our annotation schema and methodology-moving from a decision tree supporting recognition of a fallacy, to inference type, and finally to litmus tests involving frame elements to decide upon the specific fallacy-support relatively high overall annotator iaa on this challenging and generally subjective task. additionally, our prompt variation experiments support the notion that having litmus tests for particular fallacies, in the form of required frame elements, also supports f1-score gpt-4o gpt-o1 human overall .90 .89 .96-.98 -fallacy y/n .95 .95 .98-.99 -fallacy label .05 .08 .59-.73 example (3) above was the only fallacy correctly labeled by gpt-4o as well. we note that all three annotators agreed on these labels for each of these three cases. when we explore several cases where the model posited that a fallacy existed where there was none, we find that gpt-o1 most often labeled clauses as conspiracy theory fallacies: 8 of 17 predicted fallacies were assigned this label. indeed, the model seems to have the best handle on the notion of a conspiracy theory, as there was no clear set of lexical triggers associated with this set, and conceptually the false positives did involve the powerful, conspiratorial entity frame element, but no clear conspiratorial event required for annotation. next most frequently, gpt-o1 assigned scapegoat fallacies where the word "blame" was mentioned in 7 of 17 predicted fallacies. finally, ad hominem was assigned in 4 cases where there were insulting names such as "charlatan." thus, in many of these cases, while one frame element was found in the clause (often cued by a key lexical item), all required elements were not present. ## conclusion & future work when we consider our manual and model annotation results overall, we posit that model performance could be brought closer to human performance with prompting strategies as well as structured output that required frame elements and litmus tests to be passed. only if the model can provide all frame elements can the annotation of a particular fallacy be assigned. this process of requiring the model to "show its work" when it comes to the fallacy assigned is quite similar to how annotators argued for and settled disputes over fallacy labels. in addition to exploring more sophisticated prompting strategies, we are currently working to further expand our corpus to levels adequate to experiment with finetuning a model. we are eager to see if a fine-tuned model can excel at this task, or if larger models with more advanced "reasoning" capabilities can outpace even fine-tuned models given the right prompting strategies. with improved model performance over a larger corpus, we will also begin to explore if there is any difference in performance in detecting fallacies that are missteps in different reasoning types. it has been posited that llms are inductive, bottomup reasoners moving from specific observations to generalizations @xcite ; thus, we may expect performance on inductive fallacies to be superior to deductive and abductive fallacies. however, we also note an opportunity to leverage fallacy recognition evaluation in order to further explore whether or not these models are "reasoning" at all (cf. @xcite ).