Investigating the effectiveness of detecting misinformation on social media using Tshivenda language

Abstract

The spread of misinformation on social media poses a major challenge to information integrity and public discourse. This study examines the effectiveness of detecting misinformation in Tshivenda language, which is one of the underrepresented languages in South Africa. The same applies also on social media platforms. We analyse misinformation patterns, adapt existing detection techniques, and examine the influence of Tshivenda language. Through an extensive literature review, we investigated the state of the art in misinformation detection and its applicability to languages with limited digital footprints. To address this gap, we used Long Short-Term Memory (LSTM) models, a type of recurrent neural network known for capturing long-range dependencies, for misinformation detection. Our research involved training and evaluating the LSTM model on the Tshivenda and English datasets. This comparative analysis provided valuable insights into the challenges and opportunities that linguistic diversity presents in detecting misinformation. Our results shed light on the effectiveness of using LSTM models to detect misinformation in underrepresented languages. By analysing the results from the Tshivenda and English datasets, we were able to gain valuable insight into the differences in performance and the impact of linguistic variation on the accuracy of misinformation detection.