Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning

In this paper, we present our participation to the MediaEval-2021 challenge on fake news detection about coronavirus related Tweets. It consists in three subtasks that can be seen as multi-labels classification problems we solved with transformer-based models. We show that each task can be solved independantly with mutiple monotasks models or jointly with an unique multitasks model. Moreover, we propose a prompt-based model that has been finetuned to generate classifications from a pre-trained model based on DistilGPT-2. Our experimental results show the multitask model to be the best to solve the three tasks. Copyright 2021 for this paper by its authors.