Recent studies on semantic frame induction have demonstrated that the emergence of pre-trained language models (PLMs) has led to more accurate results.However, most existing studies evaluate the performance using frame resources such as FrameNet, which may not accurately reflect real-world language usage.In this study, we conduct semantic frame induction using the Colossal Clean Crawled Corpus (C4) and assess the applicability of existing frame induction methods to real-world data.Our experimental results demonstrate that existing frame induction methods are effective on real-world data and that frames corresponding to novel concepts can be induced.