Fighting the COVID -19 Infodemic with a Holistic BERT Ensemble (2021)

Abstract: This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task’s questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.

## introduction social media platforms, e.g., twitter, instagram, facebook, tiktok among others, are playing a major role in facilitating communication among individuals and sharing of information. social media, and in particular twitter, are also actively used by governments and health organizations to quickly and effectively communicate key information to the public in case of disasters, political unrest, and outbreaks @xcite @xcite @xcite . however, there are dark sides to the use of social media. the removal of forms of gate-keeping and the democratization process of the production of information have impacted the quality of the content that becomes available. misinformation, i.e., the spread of false, inaccurate, misleading information such as rumors, hoaxes, false statements, is a particularly dangerous type of low quality content that affects social media platforms. the dangers of misinformation are best illustrated by considering the combination of three strictly interconnected factors: (i) the diminishing abilities to discriminate between trustworthy sources and information from hoaxes and malevolent agents @xcite ; (ii) a faster, deeper, and broader spread than true information, especially for topics such as disasters and science @xcite ; (iii) the elicitation of fears and suspicions in the population, threatening the texture of societies. the covid-19 pandemic is the perfect target for misinformation: it is the first pandemic of the information age, where social media platforms have a primary role in the information-sphere; it is a natural disaster, where science plays a key role to understand and cure the disease; knowledge about the sars-cov-2 virus is limited and the scientific understanding is continually developing. to monitor and limit the threats of covid-19 misinformation, different initiatives have been activated (e.g., #coronavirusfacts alliance foot_0 , euvs-disinfo foot_1 ), while social media platforms have been enforcing more stringent policies. nevertheless, the amount of produced misinformation is such that manual intervention and curation is not feasible, calling for the development of automatic solutions grounded on natural language processing. the proposed shared task on covid-19 misinformation presents innovative aspects mirroring the complexity and variation of phenomena that accompanies the spread of misinformation about covid-19, including fake news, rumors, conspiracy theories, racism, xenophobia and mistrust of science, among others. to embrace the variation of the phenomena, the task organizers have developed a rich annotation scheme based on seven questions @xcite . participants are asked to design a system capable of automatically labeling a set of messages from twitter with a binary value (i.e., yes/no) for each of the seven questions. train and test data are available in three languages, namely english, arabic, and bulgarian. our team, tokofou, submitted predictions only for the english data by developing an ensemble model based on a combination of different transformer-based pre-trained language encoders. each pre-trained model has been selected to match the language va-riety of the data (i.e., tweet) and the phenomena entailed by each of the questions. with an overall f1 score of 89.7 our system ranked first 3 . ## data the english task provides both training and development data. the data have been annotated using a in-house crowdsourcing platform following the annotation scheme presented in @xcite . the scheme covers in a very extensive way the complexity of the phenomena that surrounds covid-19 misinformation by means of seven key questions. the annotation follows a specific pattern after the first question (q1), that aims at checking whether a message is a verifiable factual claim. in case of a positive answer, the annotator is presented with an additional set of four questions (q2-q5) addressing aspects such as presence of false information, interest for the public, presence of harmful content, and check-worthiness. after this block, the annotator has two further questions. q6 can be seen as a refinement of the presence of harmful content (i.e, the content is intended to harm society or weaponized to mislead the society), while q7 asks the annotator whether the message should receive the attention of a government authority. in case of a negative answer to q1, the annotator jumps directly to q6 and q7. quite interestingly, q6 lists a number of categories to better identify the nature of the harm (e.g., satire, joke, rumor, conspiracy, xenophobic, racist, prejudices, hate speech, among others). the labels of the original annotation scheme present fine-grained categories for each questions, including a not sure value. for the task, the set of labels has been simplified to three: yes, no, and nan, with this latter corresponding in some cases to the not sure value. indeed, due to the dependence of q2-q5 to a positive answer to q1, some nan values for this set of questions can also correspond to not applicable rather than to not sure making the task more challenging than one would expect. for english, the organisers released 869 annotated messages for training, 53 for development, and 418 for testing. the distribution of the labels for each question in the training data is reported in figure 1 . as the figures show, the dataset is unbalanced for all questions. while the majority of messages present potential factual claims (q1), only a tiny minority has been labelled as containing false information (q2) with a very high portion re-3 source code is available at https://git.io/jotph . ceiving a nan label, suggesting that discriminating whether a claim is false or not is a difficult task for human annotators. similar observations hold for q3-q5. q6 is a refinement of q4 about the nature of the harm. the low amount of nan values indicates a better reliability of the annotators in deciding the specific type of harms. q7 also appears to elicit more clear-cut judgements. finally, with the exception of questions q4-q7 which exhibit a weak pairwise covariance, no noteworthy correlation is discernible (refer to figure 2 ). ## system overview our system is a majority voting ensemble model based on a combination of six different transformerbased pre-trained encoders, each selected targeting a relevant aspect of the annotated data such as domain, topic, and specific sub-tasks. ## results and discussion results on the test data are illustrated in ## conclusion we participated in the covid-19 misinformation shared task with an ensemble of pre-trained bertbased encoders, fine-tuning each model for predictions in all questions and aggregating them into a final answer through majority voting. our system is indeed a strong baseline for this task showing the effectiveness of available pre-trained language models for twitter data, mixed with variants fine tuned for a specific topic (covid-19) and multiple downstream tasks (emotion detection, hate-speech, etc.). results indicate that this holistic approach to transfer learning allows for a data-efficient and compute-conscious methodology, omitting the often prohibitive computational requirement of retraining a model from scratch for a specific task, in favour of an ensemble architecture based on task/domain-similar solutions from a large ecosystem of publicly available models. with appropriate scaling of the associated dataset, a system as proposed by this paper can be suitably integrated into a human-in-the-loop scenario, serving as an effective assistant in (semi-) automated annotation of twitter data for misinformation.