Analyzing Indonesian political hoax detection system on social media using deep learning and natural language processing

The widespread dissemination of false information across social media platforms has become a pressing concern, significantly impacting how individuals perceive reality and make critical decisions. This issue is especially alarming due to the speed and scale at which misinformation can spread, often reaching large audiences within minutes. To address this challenge, Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising technologies for automatically detecting deceptive content. These tools work by analyzing linguistic cues, identifying textual irregularities, and recognizing patterns commonly found in misleading narratives. This study examines the performance of several machine learning models in detecting fake news content in the Indonesian language, utilizing NLP methods such as text preprocessing and vectorization through word embeddings. By testing these models on a labeled dataset of verified hoaxes from Indonesian sources, the research aims to improve prediction accuracy and offer scalable, automated solutions. Ultimately, the goal is to contribute to a safer, more reliable digital information ecosystem in the face of growing online misinformation.