Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of the COVID -19 Infodemic (2023)

Abstract: The spread of COVID-19 misinformation on social media became a major challenge for citizens, with negative real-life consequences. Prior research focused on detection and/or analysis of COVID-19 misinformation. However, fine-grained classification of misinformation claims has been largely overlooked. The novel contribution of this paper is in introducing a new dataset which makes fine-grained distinctions between statements that assert, comment or question on false COVID-19 claims. This new dataset not only enables social behaviour analysis but also enables us to address both evidence-based and non-evidence-based misinformation classification tasks. Lastly, through leave claim out cross-validation, we demonstrate that classifier performance on unseen COVID-19 misinformation claims is significantly different, as compared to performance on topics present in the training data.

## introduction for the majority of citizens, social media became the primary source of information during the covid-19 pandemic @xcite . while social media allowed citizens to seek information in a more timely manner, it also resulted in an 'infodemic' (who, 2020) of misinformation which has caused significant harms. therefore, while independent fact-checkers (e.g., international fact-checking network ifcn foot_1 ) played a vital role, they increasingly need ai models @xcite to help scale up and optimise the fact-checking workflows. such models, however, have been trained primarily on datasets of political and other non-covid-19 misinformation, which has impacted their accuracy in detecting and classifying covid-19 false claims. prior studies of covid-19 misinformation focused mainly on misinformation detection @xcite @xcite , the social engagement with fake news on websites and social platforms @xcite , and the ways that misinformation is countered in tweets @xcite . however, they have largely overlooked the wider online debates about covid-19 misinformation, such as the conversational threads around false covid-19 claims and the questions and comments made as part of these. it is absolutely crucial for fact-checkers to have at their disposal models that not only flag misinformation, but can also flag the comments and questions raised in online debates around false claims, so they can address them in debunks. in particular, this paper aims to address three research questions: rq1: which social media posts are propagating, questioning or commenting about a false claim? rq2: does the volume of tweets debunking a misinformation claim correlate with the volume of misinformation tweets? rq3: what are the different kinds of covid-19 misinformation spreading online? the novel contributions are: in particular, these false claims are used as the queries to extract tweets with topics that are related to the particular false claim. 2. a manually annotated fine-grained covid-19 misinformation dataset with 8 fine-grained categories that are suitable for training machine learning classification models. 3. a quantitative analysis of the fine-grained categories throughout a 10-month period of the pandemic and particularly investigating the different kinds of misinformation. evidence-based classification aims to classify already verified misinformation given ifcn @xcite . the harder, non-evidence based task finds social media posts that are likely to be misinformation; however these posts may require human verification. ## dataset and annotation the overall pipeline of dataset annotation is shown in figure 1 . in general, we first collect covid-19 related tweets based on a set of keywords. next, we use a subset of fact-checked misinformation claims from the ifcn as queries to retrieve related tweets. the collected tweets are then annotated based on fine-grained categories, and the agreement rates between annotators are evaluated. ## misinformation classification experiments in this section, we conduct a benchmark experiment for our annotated twitter misinformation classification dataset. this experiment includes three tasks that represent three different misinformation classification scenarios. the task detail and the experiment settings are discussed in section 4.1. then, we introduce the baseline models and model configurations in section 4.2. finally, the experimental results are discussed in section 4.3. ## conclusion this paper presents a fine-grained covid-19 misinformation dataset, which comprises 1,722 manually annotated tweets across eight categories. each tweet in the dataset undergoes triple annotation, resulting in a substantial agreement with an averaged krippendorff's alpha of 0.67. analysis of the dataset reveals that misinformation tweets have a similar spread rate to debunk tweets. additionally, we observe that both question and comment tweets have positive correlation with misinformation and debunk tweets. notably, our findings indicate that misinformation tweets can include urls from high-credibility sources, shedding light on the potential challenges in identifying misinformation solely based on the source credibility. furthermore, the paper presents three misinformation classification benchmark experiments: 1) non-evidence-based, 2) evidence-based, and 3) fine-grained classification. the results of these experiments demonstrate that the baseline models perform well in the standard cross-validation setting across all classification experiments. however, the classification performance dropped significantly in the leave claim out cross-validation setting. this emphasises the need for regular updates to the training instances to ensure consistent classification performance over time.