With the constant spread of misinformation on social media networks, a need has arisen to continuously assess the veracity of digital content. This need has inspired numerous research efforts on the development of misinformation detection (MD) models. However, many models do not use all information available to them and existing research contains a lack of relevant datasets to train the models, specifically within the South African social media environment. The aim of this paper is to investigate the transferability of knowledge of a MD model between different contextual environments. This research contributes a multimodal MD model capable of functioning in the South African social media environment, as well as introduces a South African misinformation dataset. The model makes use of multiple sources of information for misinformation detection, namely: textual and visual elements. It uses bidirectional encoder representations from transformers (BERT) as the textual encoder and a residual network (ResNet) as the visual encoder. The model is trained and evaluated on the Fakeddit dataset and a South African misinformation dataset. Results show that using South African samples in the training of the model increases model performance, in a South African contextual environment, and that a multimodal model retains significantly more knowledge than both the textual and visual unimodal models. Our study suggests that the performance of a misinformation detection model is influenced by the cultural nuances of its operating environment and multimodal models assist in the transferability of knowledge between different contextual environments. Therefore, local data should be incorporated into the training process of a misinformation detection model in order to optimize model performance.

翻译：随着虚假信息在社交媒体网络中持续传播，迫切需要持续评估数字内容的真实性。这一需求催生了大量关于虚假信息检测（MD）模型的研究。然而，许多模型并未充分利用可获得的信息，且现有研究缺乏用于训练模型的相关数据集，尤其是在南非社交媒体环境下。本文旨在探究MD模型在不同语境环境间的知识迁移能力。本研究提出了一种能够在南非社交媒体环境中运行的多模态MD模型，并引入了南非虚假信息数据集。该模型利用多种信息源进行虚假信息检测：即文本和视觉元素。它采用双向编码器表示（BERT）作为文本编码器，残差网络（ResNet）作为视觉编码器。该模型在Fakeddit数据集和南非虚假信息数据集上进行训练和评估。结果表明，在南非语境环境下，将南非样本纳入模型训练可提升模型性能，且多模态模型保留的知识量显著高于纯文本和纯视觉单模态模型。本研究表明，虚假信息检测模型的性能受其运行环境的文化细微差别影响，而多模态模型有助于在不同语境环境间实现知识迁移。因此，应将本地数据纳入虚假信息检测模型的训练过程以优化模型性能。