Sikosana, M., Ajao, O., & Maudsley-Barton, S. (2024). A Comparative Study of Hybrid Models in Health Misinformation Text Classification. In 4th International Workshop on OPEN CHALLENGES IN ONLINE SOCIAL NETWORKS (OASIS ’24) (pp. 1–8). Poznan, Poland: ACM. https://doi.org/10.1145/3677117.3685007
This research paper investigates the effectiveness of various machine learning (ML) and deep learning (DL) models in detecting COVID-19 misinformation on online social networks (OSNs). The authors aim to identify the most effective computational techniques for this task and contribute to the development of tools for combating health misinformation.
The study uses the "COVID19-FNIR DATASET," a balanced dataset of true and fake news related to COVID-19. The authors train and test a range of ML classifiers (Naive Bayes, SVM, Random Forest, etc.), DL models (CNN, LSTM, hybrid CNN+LSTM), and pretrained language models (DistilBERT, RoBERTa) on this dataset. They evaluate the models' performance using metrics such as accuracy, F1 score, recall, precision, and ROC.
The study concludes that DL and hybrid DL models are more effective than conventional ML algorithms for detecting COVID-19 misinformation on OSNs. The authors emphasize the importance of advanced neural network approaches and large-scale pretraining in misinformation detection.
This research contributes to the growing body of knowledge on automated misinformation detection, particularly in the context of public health crises like the COVID-19 pandemic. The findings have implications for developing more effective tools and strategies to combat the spread of harmful health misinformation online.
The study focuses specifically on COVID-19 misinformation and may not generalize to other types of misinformation or online platforms. Future research should explore the models' effectiveness on different datasets, languages, and misinformation types. Additionally, the authors suggest investigating methods to adapt these models to the evolving nature of OSNs and misinformation tactics.
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by Mkululi Siko... : arxiv.org 10-10-2024
https://arxiv.org/pdf/2410.06311.pdfDaha Derin Sorular