The lack of sufficient labeled examples for devising and training sophisticated approaches to combat PSM accounts is still one of the foremost challenges facing social media firms. In contrast, unlabeled data is abundant and cheap to obtain thanks to the massive user-generated data produced on a daily basis. This chapter proposes a semi-supervised causal inference PSM detection framework, SemiPsm, to compensate for the lack of labeled data for identifying PSM users. The proposed method leverages unlabeled data in the form of manifold regularization and only relies on cascade information from users’ activities. This is in contrast to the existing approaches that use exhaustive feature engineering (e.g., profile information, network structure, etc.). Evidence from empirical experiments on the ISIS-B dataset from previous chapters suggests promising results of utilizing unlabeled instances for detecting PSMs.