Image sharing chain detection aims to recover the sharing history of an image downloaded from online social networks (OSNs), including the ever-shared OSNs and their orders, which is an important task in the multimedia forensics community. Most of the existing algorithms directly treat the sharing chain detection as a classification problem by simply assigning a unique label to each sharing chain. Such a strategy though seems straightforward, it ignores the inherent properties of the sharing chain which can be regarded as a time sequence that carries the sharing history of an online image. In this paper, we suggest a new sharing chain detection framework via Sequence-to-Sequence (Seq2Seq) model. Different from previous classification based approaches, our model detects the sharing chain of online image progressively via a decoder. This progressive manner can fully utilize the decoded chain, which is embedded into a series of learned representations. Experimental results show that our method can detect sharing chains involving up to three OSNs, and exhibits much better performance than conventional ones.