This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internet-connected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often cluttered with clickbait news. Leveraging state-of-the-art bidirectional encoder representation from transformers (BERT), a lightweight web application is developed. This study offloaded the computing resources needed to train the model on a separate instance of virtual server and then deployed the trained model on the cloud, while the client-side application only needs to send a request to the API and the cloud server will handle the rest, often known as three-layer architecture. This study designed and developed a web-based application to detect clickbait in Indonesian using IndoBERT as its language model. The application usage and potentials were discussed. The source code and running application are available for public with a performance of mean receiver operating characteristic-area under the curve (ROC-AUC) of 89%.
|Number of pages||7|
|Journal||IAES International Journal of Artificial Intelligence|
|Publication status||Published - Dec 2022|
- Adult literacy
- Digital divide
- Natural language processing