Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets (2022)

Abstract: False medical information on social media poses harm to people’s health. While the need for biomedical fact-checking has been recognized in recent years, user-generated medical content has received comparably little attention. At the same time, models for other text genres might not be reusable, because the claims they have been trained with are substantially different. For instance, claims in the SciFact dataset are short and focused: “Side effects associated with antidepressants increases risk of stroke”. In contrast, social media holds naturally-occurring claims, often embedded in additional context: "‘If you take antidepressants like SSRIs, you could be at risk of a condition called serotonin syndrome’ Serotonin syndrome nearly killed me in 2010. Had symptoms of stroke and seizure.” This showcases the mismatch between real-world medical claims and the input that existing fact-checking systems expect. To make user-generated content checkable by existing models, we propose to reformulate the social-media input in such a way that the resulting claim mimics the claim characteristics in established datasets. To accomplish this, our method condenses the claim with the help of relational entity information and either compiles the claim out of an entity-relation-entity triple or extracts the shortest phrase that contains these elements. We show that the reformulated input improves the performance of various fact-checking models as opposed to checking the tweet text in its entirety.

## introduction people use social media platforms like twitter to discuss medical issues. this can expose them to false health-related information and poses immediate harm to people's well-being @xcite . while the necessity for fact-checking biomedical or scientific information has been recognized and addressed in recent years, naturally occurring arguments and claims as they are shared by social media users have received less attention. unfortunately, systems trained on datasets from other domains might not be reusable: the datasets that underly existing pretrained models work with atomic, edited or summarized claims (e.g., from datasets like scifact, @xcite , cover claims that have been selected to be well-formed @xcite , or contain editorial content such as news headlines @xcite . examples 1-5 in this mismatch motivates extracting a checkworthy main claim from user-generated content before continuing with fact-checking. this claim detection task, which is also a central task in argument mining, can be addressed as a sequence labeling problem @xcite . while this approach requires dedicated annotated data, we propose an alternative that requires an entity annotation and relation detection system -something that has been developed for various purposes across domains @xcite @xcite @xcite . we hypothesize that the main information relevant to a claim is encoded in entities and their relations, because they convey the key semantic information within a statement and describe how they interact with each other. for our approach we propose to use that information to either find the claim token sequence or to generate a sentence representation based on entity and relation classes. our results show that entity-based claim extraction supports factchecking for user-generated content, effectively making it more accessible to multivers @xcite , an architecture recently suggested for scientific claim verification. ## methods with this work we investigate if knowledge about biomedical entities allows us to extract a concise claim representation from user-generated text that enables fact-checking systems to predict a verdict. to explore this, we suggest two methods to extract and construct entity-based, claim-like statements. we assume we have a sequence of tokens @xmath0, . . . , t n ). in addition, we have a set of m annotations representing claims as triples. we reduce the claim to what we hypothesize to be its core components: two medical entities and the relation between them. we hypothesize that the entities express the most relevant information with regard to the claim. in this representation the claim is a concatenation • of the subject entity tokens, the name of the relation r and the object entity tokens: this approach ignores tokens that are not part of the relation or entity annotation. extracting claim sequences. alternatively, we extract a subsequence from the original text. for each annotation a in t, we apply this retains the way the author of the original text chose to express the relation, including all tokens that are mentioned between the entities. commonly, this also involves words that indicate the relation class, but we do not ensure that. figure 1 shows examples of both condensation methods. the example is taken from @xcite . ## experiments we investigate whether we can reduce the complexity of user-generated claims in order to make the information that they convey accessible to pretrained "off-the-shelf" fact-checking models and circumvent the necessity of custom training data and specialized models. we specifically explore the use of entity information to formulate a condensed version of a claim (see section 3). more concretely, we compare how the claim representation impacts the performance of a factchecking model. ## analysis and discussion we aim to understand in which cases condensing the claim is helpful and when it harms the in total, there are 54 instances in which both claim inputs lead to a correct label. in those instances, the tweet itself tends to be fairly short (see ex. 1a) or relatively well-formed (see ex. 1b). there are 74 instances in which the condensed claim sequence produces a correctly predicted label while the check based on the full tweet input does not lead to a correct result. for 66 out of 74, we observe that the label flips from nei to the correct label (see ex. 2a). this shows that the condensation can make the evidence more accessible to the fact-checker. in addition, ex. 2b shows how a condensed claim is assigned a correct label, while the full tweet is not. this might be the case because the claim is presented as a question in the tweet. in 28 cases condensing the claim leads to an incorrect prediction while checking the full tweet leads to a correct output. in 20 cases, condensing the claim changes the predicted label from the gold verdict to nei (see ex. 3a). this indicates that condensation can render evidence unusable. in example 3b the condensation actually misrepresents the statement because it cuts of the phrase 'no evidence' before the claim. we recognize that this is a potential pitfall of the claim extraction methods. there are 108 instances where both claim types lead to incorrectly predicted labels. in 90 out of 108 cases, both are labeled with nei. the evidence did not provide sufficient information to check the claim. example 4a exemplifies that, to a certain degree, the nei label makes sense. the evidence (see ## conclusion and future work based on the substantial mismatch between the biomedical claims as they are most typically expected by existing fact-checking models and the nature of real-world, user-generated medical statements made on twitter, we propose to extract entity-based claim representations. we use the entities as the core information relevant to the claim, and extract condensed claims from tweets. when presented with the adapted claim input, the fact-checking models we experiment with are able to verify the claims more reliably as opposed to when they are tasked to infer a verdict for a full in this study, we focused the analysis on an existing dataset with a comparably narrow focus. while we intuitively believe that the findings also hold for other domains, this remains to be proven. therefore we propose that future work explores entity-and relation-based claim extraction for other types of medical relations. covert focuses on causative claims which are by design of the dataset explicitly mentioned in the tweet. exploring if claims about other types of relations can be extracted in a similar manner is up to future research. similarly, it is important to explore how this method translates to statements with more than one entity-relation-entity triple. another limitation of our analysis is its focus on one fact-checking architecture. it is important to evaluate if the impact of claim condensation carries over to other claim checking methods. a possible alternative to our approach (change the claim at test time) could also be to adapt the system (adapt the claims at training time). the degree of which the difference in genre and structure of the evidence document might impact the models' performances is another important perspective for future research. finally, we performed studies based on correct annotations of entities. while this is a reasonable approach in a research environment, it is important to explore the impact of error propagation from a named entity recognizer to claim condensation. apart from verdict prediction, entity-based claim representation could also facilitate discovering suitable evidence for user-generated medical content as entity knowledge has been shown to benefit evidence retrieval as well @xcite .