In recent years, online social networks (OSNs) have become extremely popular and been one of the most common ways for storing and distributing images. Naturally, such widespread availability of OSN makes it a viable channel for transmitting additional data along with the image sharing. However, various lossy operations, e.g., resizing and compression, conducted by OSN platforms impose great challenges for designing a robust watermarking scheme over OSN shared images. In this paper, we tackle this challenge and propose a robust high-capacity watermarking technique, by using Facebook as a representative OSN. To achieve the satisfactory robustness, we first probe into Facebook and recover the image manipulation mechanism via a deep convolutional neural network (DCNN) approach. Assisted with the precise knowledge on the lossy channel offered by Facebook, we then suggest a DCT-domain image watermarking method that is highly robust against the lossy operations on Facebook, even without any error correcting codes (ECC). The proposed technique is also extended to other popular OSNs, e.g., Wechat and Twitter. Extensive experimental results are provided to show the superior performance of our method in terms of the embedding capacity, data extraction accuracy, and quality of the reconstructed images.