Public and governmental concerns over online rumors’ widespread diffusion and deceptive impact on social media have increased. For users to obtain accurate information and preserve social peace, finding and controlling social media rumors is challenging. Automatically, identifying fake news (FN) is a critical yet challenging topic that is still little understood because the consequences are so high. The text, visual features, the acceptance of the user’s reply, stance, and social context are a few aspects of FN that are universally acknowledged. Current research has concentrated on modifying results to one specific trait, which has been partially the reason for their success. This article proposes Fakefind, a convolutional neural network (CNN) + recurrent neural networks (RNNs) hybrid model that integrates multimodal features for efficient rumor detection (RD). Additionally, the stance is extracted from indirectly implied postreply pairs using a CNN-based knowledge extractor (KE), and the stance representations are integrated for FN detection (FND). Extensive research findings are based on three multimedia rumor datasets from Weibo, Fakeddit, and PHEME. The outcomes show how well the recommended Fakefind identifies rumors with multimodal content.