A Semi-Supervised Machine Learning Framework for Detecting Political Hoaxes Based on Keyword-Guided Clustering

The spread of political hoaxes poses a significant threat to democratic integrity and social stability. This study proposes an efficient and structured machine learning approach to detect political hoax content. The text data undergoes preprocessing, including normalization, removal of punctuation and numbers, tokenization, and stopword elimination. Numerical representation is generated using Term FrequencyInverse Document Frequency (TF-IDF), followed by clustering using the K-Means algorithm to group the text based on feature similarity. The core contribution of this research is the development of an automatic labeling strategy based on political keywords, applied after the clustering stage. Unlike previous studies that rely on pre-labeled datasets or stop at unsupervised clustering, this approach introduces a contextual labeling process to distinguish between political and non-political hoaxes by identifying keyword occurrences within the clustered data. The automatically labeled data is then classified using three classical machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), and Naive Bayes. The models are evaluated to assess their ability to accurately detect political hoaxes. Among them, SVM demonstrates the highest and most consistent classification performance, indicating its suitability for this task. The proposed integrated pipeline proves effective in addressing the challenge of unlabeled data. This study contributes methodologically to the development of adaptive and efficient hoax detection systems that are applicable across various socio-political contexts.