Two Stage Transformer Model for COVID -19 Fake News Detection and Fact Checking (2020)

Abstract: The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user queries about particular COVID-19 claims. The second model verifies the level of “truth” in the queried claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.

## introduction electronic means of communication have helped to eliminate time and distance barriers to sharing and broadcasting information. however, despite all its advantages, faster means of communication has also resulted in extensive spread of misinformation. the world is currently going through the deadly covid-19 pandemic and fake news regarding the disease, its cures, its prevention and causes have been broadcast widely to millions of people. the spread of fake news and misinformation during such precarious times can have grave consequences leading to widespread panic and amplification of the threat of the pandemic itself. it is therefore of paramount importance to limit the spread of fake news and ensure that accurate knowledge is disseminated to the public. in this work, we propose a robust, dynamic fake news detection system, that can not only estimate the "correctness" of a claim but also provides users with pertinent information regarding the said claim. this is achieved using a knowledge base of verified information that can be constantly updated. previous work on fake news detection has primarily focused on evaluating the relationship measured via a textual entailment task between a header and the body of the article. however, such a method is insufficient for identifying specific fake news claims without any knowledge of the facts relevant to the claim. this warrants the use of a new dataset specific to the covid-19 pandemic. developing a solution for such a task involves generating a database of factual explanations, which becomes our knowledge base, that serves as ground truth for any given claim. we compute the entailment between any given claim and explanation to verify if the claim is true or not. querying for claim, explanation pairs for each explanation in our knowledge base is computationally expensive and slow, so we propose generating a set of candidate explanations which are contextually similar to the claim. we achieve this by using a model trained with relevant and irrelevant claim explanation pairs, and using a similarity metric between the two to match them. therefore, in the pipeline, firstly, for a given claim, a set of candidate explanations from the knowledge base have to be fetched in real-time. then, the claim validated for truth using relevant candidate explanations. in this work, we have explored the use of transformer @xcite based models to both fetch relevant explanations as well as measure the entailment between a given claim and a factual explanation. we then evaluate our model on the basis of count of relevant explanation results fetched as well as the accuracy in verifying a given claim. we demonstrate the effectiveness of pre-trained multi-attention models in terms of overall accuracy when compared with other natural language processing (nlp) baselines while maintaining near real-time performance. ## related work the paper @xcite uses traditional approaches with a simple classifier model that makes use of term frequency(tf), term frequency and inverse document frequency(tf-idf), and cosine similarity between vectors as features to classify fake news. they have provided a baseline for fake news stance detection on fake news challenge (fnc-1) dataset foot_0 . we have implemented their approach on our dataset and results can be seen in ## dataset using an existing misinformation dataset will not serve as a reliable knowledge base for training and evaluating the models due to the recent and uncommon nature i.e., the vocabulary used to describe the disease and the terms associated with the covid-19 pandemic. it is important to generate real and timely datasets to ensure accurate and consistent evaluation of the methods. to overcome this drawback, we manually curated a dataset specific to covid-19. our proposed dataset consists of 5500 claim and explanation pairs. we describe the collection and annotation process in section 3.1. ## methodology the architecture consists of a two stage model, we will refer to the first model as "model a" and the second model as "model b". the objective of model a is to fetch the candidate "true facts" or explanations for a given claim, which are then evaluated for entailment using the model b. next, we describe the training procedure as well as intended run time behaviour for both model a and model b. 4.1 model a first, to fetch relevant explanations, we train our transformer model on a binary sentence entailment task, where the claims and explanations are the two sentences fed in as input separated by a [sep] tag. we generate negative claim-explanation pairs through random sampling to ensure that equal proportions of positive and negative pairs are present. training multi-attention network with our covid-19 specific data enables the model to capture long-range correlations between the vector representations of claims and explanations of similar contexts. we train our models with a base encoder and a sequence classification head on top for binary classification of the labels. the model is trained to optimise the cross entropy loss. through our experiments, we find that, on this trained model, if we generate embeddings for a single sentence (either claim or explanation individually) and compare matching [claim, explanation] embeddings using the cosine similarity metric, there is a distinction in the distribution of similarity scores between related and unrelated [claim,explanation] pairs. therefore, for faster near real-time performance, we cache the embeddings for all our explanations (knowledge base) beforehand, and compute the cosine similarity between the claim and the cached embeddings of the explanations. the vector of the [cls](the start of sentence) token of the final layer works as a strong representation of the entire sentence, although we found that taking element-wise mean over all the token vectors leads to better performance. we fetch the top explanations for any given claim exceeding a certain threshold of sentence similarity as there could be several explanations relevant for a given claim. this threshold is determined on the basis of the summary statistics of the cosine similarity metric between the claim and relevant explanations in the validation set, as described in section 5.3. these retrieved explanations serve as candidates for verifying the accuracy of the claim through model b.