Internet users are regularly using Online social networks (OSNs). However, spam originating from various sources causes damage to less security-savvy users. Earlier counteraction withstand OSN spam from different angles. Due to the wide range of spam, there is rarely any current procedure that can independently detect the majority of OSN spam. In this paper, we empirically analyze the textual pattern of a large collection of OSN spam. An inspiring finding is that the majority (e.g., 76.4% in 2015) of the collected spam is generated with underlying templates. Based on the analysis, we propose tangram, an OSN spam filtering system that performs online inspection on the stream of user-generated messages. Tangram extracts the templates of spam detected by existing methods and then matching messages against the templates toward the accurate and the fast spam detection. It automatically divides the OSN spam into segments and uses the segments to construct templates to filter future spam. Results on Twitter and Facebook data sets show that tangram is accurate and can rapidly generate templates to throttle newly emerged campaigns. Furthermore, we analyze the behavior of detected OSN spammers. We find a series of spammer properties—such as spamming accounts are created in bursts and a single active organization orchestrates more spam than all other spammers combined—that promise more widespread spam counteraction