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.

本研究旨在调查虚假信息检测模型在不同背景环境下的知识可转移性，为南非社交媒体环境提供一种多模态虚假信息检测模型，并引入南非虚假信息数据集。结果表明，将南非样本用于模型训练可以提高模型性能，并且多模态模型比文本和视觉单模态模型保留更多知识。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，并且多模态模型有助于在不同背景环境之间的知识转移。因此，在虚假信息检测模型的训练过程中应该纳入本地数据以优化模型性能。