Spoiler detection in TV program tweets

In this work, we introduce a simple and powerful method of spoiler detection based on four representative features, which are significant indicators of spoilers. To identify and utilize four features, we conduct a precise analysis on real-world tweet data, and we build an SVM-based prediction model based on the result. Using tweets about Dancing with the Stars, and the final of the 2014 World-Cup, we evaluate the effectiveness of the proposed methods on spoiler detection tasks. According to the result, our method achieves greater precision than the competitors while maintaining a comparable recall performance. At the same time, our method outperforms the competitors in terms of processing time, showing that our method is sufficiently lightweight for application to the web-browser. Furthermore, to reduce the labeling cost, we introduce a semi-supervised approach that automatically re-trains the prediction model based on a small amount of labeled data. The experimental results show that the semi-supervised approach delivers performance comparable to that of the previous model.