The proliferation of social bots poses a persistent threat to online social platforms, making accurate and efficient detection increasingly critical. Although recent graph-based methods have achieved notable progress by modeling user interactions, many of them implicitly assume neighborhood consistency and degrade in sparse, directed, and heterophilic social graphs, where a user's neighborhood may poorly reflect its own attributes. In this work, we propose SNBot, a novel social bot detection framework that explicitly models the discrepancy between node self-representations and their neighborhood embeddings. SNBot first learns unified self-representations from multi-modal user attributes through a lightweight type-aware encoding scheme. It then performs message passing on an augmented directed graph using a bidirectional aggregation mechanism, which separately captures incoming and outgoing interactions to better characterize asymmetric behaviors. By preserving and exploiting self–neighborhood representation discrepancies rather than over-smoothing them, SNBot produces more discriminative node representations. Extensive experiments on multiple real-world benchmark datasets demonstrate that SNBot consistently outperforms state-of-the-art methods.