Defending against Misinformation: Evaluating Transformer Architectures for Quick Misinformation Detection on Social Media.

Prior to the technological revolution, reliance on traditional sources such as newspapers and other mass communication channels was the predominant method of consuming news. With the pervasive influence of social media, traditional news sources have undergone significant displacement, yielding predominance to platforms rooted in social media networks. Informal news propagation through social media has been observed to be fast in nature. With the information propagating fast, the misinformation also propagates rapidly on social media. If the misinformation is not detected early, it can cause serious problems. Thus the problem of identifying misinformation at the onset needs much attention. This paper explores various architectures for detecting misinformation, employing deep transfer learning techniques. The study utilizes transformer architectures, namely MPNet, SentenceT5, and Generalizable T5-based dense Retrievers, to generate embeddings. These architectures have undergone pre-training and fine-tuning on diverse tasks. The system's performance is assessed using a benchmark dataset of misinformation from Reddit, referred to as 'Fakeddit'. When tested on unseen data, the models perform well, and outperform previously reported baselines and other models on the given dataset with textual modality. The results indicate that transfer learning with zero-shot or few-shot learning can prove very cost-effective and efficient for the problem of misinformation detection. [ABSTRACT FROM AUTHOR]