This study investigates the impact of rumor alterations on detectability; we identify that sentiment is an important qualifier for rumor detection models, and the alteration of a rumor’s sentiment can produce more evasive rumors. Using the PLAN rumor detection model and modified PHEME, Twitter15, and Twitter16 datasets, we show that altering positive and neutral sentiments reduces detection metrics by up to 1.8%. Rephrasing rumors with non-rumor content has the most significant effect, decreasing accuracy, precision, recall, and F1 by up to 5.7%. Our findings highlight the challenges of detecting altered rumors and introduce new methodologies for generating altered rumor datasets, advancing rumor detection research and combating misinformation.