Social media platform frequently experiences incidents where accounts are compromised. These incidents involve attempts to misappropriate personal information, deceive others into trusting fraudulent URLs, or manipulate public sentiment by exploiting stolen accounts. Little research has been done on compromised account detection. Some approaches adopt an incremental approach at message level by comparing incoming messages against established normal behavior model for detection. The other approach utilizes supervised algorithms at the account level by binary classification into normal and compromised accounts. This paper aims to investigate compromised account detection over social media. We proposed the segmented-based supervised approach by leveraging time series change point detection from statistical community. The change point detection algorithm attempts to discover the suspicious compromised intervals. Based on the change point detection, a new feature, change point feature is proposed to capture user’s behavior changing pattern. Moreover, the interest feature based on a user’s activities in his/her interested groups and the polarity feature that captures the user’s polarity based on like/dislike behaviors are proposed. Experiments conducted on both real and synthetic datasets, demonstrate the superior accuracy of the proposed method.