What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification (2024)

Abstract: Verifying biomedical claims fails if no evidence can be discovered. In these cases, the fact-checking verdict remains unknown and the claim is unverifiable. To improve this situation, we have to understand if there are any claim properties that impact its verifiability. In this work we assume that entities and relations define the core variables in a biomedical claim’s anatomy and analyze if their properties help us to differentiate verifiable from unverifiable claims. In a study with trained annotation experts we prompt them to find evidence for biomedical claims, and observe how they refine search queries for their evidence search. This leads to the first corpus for scientific fact verification annotated with subject–relation–object triplets, evidence documents, and fact-checking verdicts (the BEAR-FACT corpus). We find (1) that discovering evidence for negated claims (e.g., X–does-not-cause–Y) is particularly challenging. Further, we see that annotators process queries mostly by adding constraints to the search and by normalizing entities to canonical names. (2) We compare our in-house annotations with a small crowdsourcing setting where we employ both medical experts and laypeople. We find that domain expertise does not have a substantial effect on the reliability of annotations. Finally, (3), we demonstrate that it is possible to reliably estimate the success of evidence retrieval purely from the claim text (.82F _1 ), whereas identifying unverifiable claims proves more challenging (.27F _1 )

## introduction verifying scientific claims in user-generated content is sometimes unsuccessful, because no supporting or refuting evidence can be found. in these cases, the claim remains unverifiable. previous work shows that both evidence retrieval and claim verification, the two core steps in automatic fact verification, are more robust for concisely formulated claims that have been extracted from noisy context compared to verifying user-generated claims directly @xcite . based on these findings, we hypothesize that breaking down claims into smaller units increases our understanding which properties impact verifiability. this knowledge is key to improving factchecking (fc) systems. to this end, we assume that biomedical entities, e.g., mentions of treatments or medical conditions, and the relations between them (causes, is-a-side-effect etc.) make up the core variables that define the claim's anatomy. we analyze if these variables are connected to the claim's verifiability, i.e., that there are reoccurring patterns with respect to which types of claims tend to be supported, refuted, or unverifiable. while disciplines like argument mining have an evolved understanding of claim properties @xcite , biomedical claims are poorly understood. the data resources to perform analyses do not exist yet: there is no corpus which is annotated both with (i) biomedical entities in relation that constitute claims and (ii) evidence and the veracity label it leads to. to create such a resource, we perform (1a) an annotation study of medical tweets in which we observe carefully trained in-house annotator's behavior to find evidence for entity-centered claims, (1b) a statistical analysis of the connection between entities/relations and the successful evidence retrieval, (2) a comparison of the in-house annotators' performance to crowdsourcing, in which we task laypeople and medical experts to verify the same claims. finally, we (3) compare the performance of a fine-tuned transformer model to estimate the checkability of a claim. we contribute bear-fact, a novel twitter 1 dataset for biomedical fact verification. it consists of 1,448 fact-checked claims, evidence documents and structured entity/relation information. we answer the following research questions: rq1a which properties, i.e., entity-relation patterns, make a claim (un)verifiable? rq1b how can we use medical entities in the claims as meaningful search queries for evidence discovery? rq2 what is the impact of the annotation setting, i.e., domain knowledge and crowdsourcing on annotation quality? rq3 can we predict the verifiability, i.e., the likelihood that evidence for a claim exists, purely from the claim? for rq1a ( §3.1), we find that entity-relation patterns in claims are connected to verifiability. claims conveying a positive relation (e.g., causeof ) are more successfully fact-checked and more frequently supported compared to their negative counterpart (not-cause-of ). for rq1b ( §3.2), we find that study participants predominantly change an entity-centric predefined query by reformulating entity realizations to canonical names. for rq2 ( §4), we observe that domain expertise does not have a substantial effect on the reliability of fact-checking annotations. finally, the hypothesis in rq3 holds to some degree ( §5): fine-tuning a roberta model to differentiate between verifiable and unverifiable claims is reliable for the verifiable class (.82 f 1 ). detecting unverifiable claims is more challenging (.27 f 1 ). ## annotation study we design an annotation study with two goals: (1) to construct a resource that enables us to explore claim properties, i.e., the role of entities and relations in the fact verification process. (2) to observe how fact-checkers modify entity-based search queries during the evidence retrieval process. we construct a dataset with two annotation layers: (a) fact verification annotation, i.e., claims checked against evidence, and (b) structured knowledge, i.e., entity and relations. we build our dataset on top of bear @xcite , a corpus of english tweets annotated with biomedical entities and relations. ## claim characteristics & evidence discovery we hypothesize that entity-relation properties in a claim are connected to its verifiability. to investigate this, we explore the following questions: which claim relations are (un)verifiable? to investigate if there are specific relation types in claims that tend to be supported, refuted or unverifiable, we analyze the distribution of verdicts across each relation type. figure 2a shows the results. each row in the heatmap represents a relation class, each column represents a fact-checking verdict. each cell depicts the percentage of claims that express the relation and verdict. we observe that for the positive relations, e.g., cause-of, prevents or positive-influence-on the distribution is dominated by the overall verdict distribution within bear-fact, meaning the majority of claims fall into the supported category, followed by unverifiable and refuted. however, for their negative counterparts, e.g., not-cause-of, does-not-prevent etc., we observe the opposite. the dominant verdict class is refuted or unveri-fiable. the exception is the pair positive/negative influence on where the distribution of the negative relation is very similar to its positive counterpart. to test if the distribution of positive and negative variants of a relation are in fact different, i.e., there is no relation between the two distributions, we compute the chi-square statistic foot_9 for relation classes @xmath0, (doesnot-)treat, pos./neg.-influence-on). the results show that for two out of three relation pairs, i.e., (not-)cause-of and (does-not-)treat) the distributions are in fact unrelated (@xmath1.05). which claim entities are (un)verifiable? figures 2b and 2c illustrate the distribution of entity types across each fact-checking verdict. from fig. 2b , we observe that the vast majority of claims use a medical condition (medc) or treatment mention (treat_drug, treat_therapy) as their subject. the distribution across the verdicts for the majority of entity classes mirrors the distributing of verdicts in the overall dataset. in fig. 2c , we observe that claim objects are almost exclusively mentions of medical conditions. this is most pronounced in (partially)refuted claims where 88 out of 98 (partially)refuted claims make a claim. across all verdicts, environmental factors are almost exclusively claim objects. when considering the entity distributions, note that to a certain degree the relation type dictates the subject and object entity type in a triplet. ## conclusion with this paper, we contribute a better understanding of what makes claims (un)verifiable. to this end we design a study that tasks annotators with varying levels of expertise to search for evidence and label claims that have entity/relation annotations. in an in-depth analysis of the resulting resource we find that claims with particular relations are more challenging to find evidence for and successfully assign fact-checking verdicts. this leads to important future work, namely to focus on methods that are able to find evidence also for negated claims (x-does-not-cause-y). through the study we also observe that some specific topics appear to be more challenging, including environmental factors. we suggest that future work studies which data bases are promising sources to provide evidence for specific biomedical topics. presumably, pubmed is not equally well suited across topics. we further analyze if the expertise level of annotators has an impact on the annotation quality and how far the annotations by various groups overlap. evidently, domain expertise leads to more carefully selected evidence (which might also be more accurately selected), but not to a too large difference in verdicts. this aspect requires further studieshow exactly does an evidence document need to relate to a claim to allow for a correct verdict? does, perhaps, the inference procedure vary between annotators for a good reason? following a perspectivist approach, the diversity in verdicts should be carefully investigated in future studies. finally, we perform a modeling study to understand if we can develop a system supporting the annotation process. we find that a text classifier is surprisingly successful to predict if a claim is likely to have evidence -with a nearly perfect recall and a high precision. future work is required to study how such classifier can be used in an annotation setup. we assume that not restricting the time of annotators to find evidence for such claims would be an appropriate design decision. apart from that, future work should explore other modeling approaches such as few-shot prompting to explore the capabilities of llms for our task. with respect to both understanding claim and evidence properties as well as modeling, we further need to explore how to handle complex claims that may not follow the subject-relation-object structure we are investigating in this work.