TAF PUBLISHING

At present, numerous people watch prerecorded TV programs as daily leisure. Concerning soap operas or sports, the viewers may not want to be informed about the results before watching the programs; however, they may check tweets on devices, such as smartphones, which can accidentally include contents referring to spoilers. To avoid reading such content, several approaches were proposed to detect spoilers in texts (both long and short ones), including tweets. In the study by Jeon et al. focused on detecting spoilers in tweets, only one person attached labels to tweets, and the labeled tweets were used to train detectors. The trained detector was tuned for one person and, therefore, could be unsuitable for others. A tweet published in the middle of a baseball game can be considered a spoiler by some people and not by others; therefore, a personalized detection method is preferred. However, to the best of our knowledge, none of the related studies has considered such a personalized approach. To address this problem, we propose a semi-supervised approach to detect spoilers in tweets using a support vector machine (SVM) in which each user attaches labels to tweets. After that, SVM executes the same procedure for other unlabeled tweets through bootstrapping. To verify the suitability of the proposed approach to personalize detectors, we conducted an experiment in which two participants were asked to attach labels to tweets. The experimental results indicate that this approach is efficient for personalized detection based on the Mann-Whitney U test.