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Spam content is surging with an explosive increase of user generated content (UGC) on the Internet. Spammers often insert popular keywords or simply copy and paste recent articles from the Web with spam links inserted, attempting to disable content-based detection. In order to effectively detect spam in user generated content, we first conduct a comprehensive analysis of spamming activities on a large commercial UGC site in 325 days covering over 6 million posts and nearly 400 thousand users. Our analysis shows that UGC spammers exhibit unique non-textual patterns, such as posting activities, advertised spam link metrics, and spam hosting behaviors. Based on these non-textual features, we show via several classification methods that a high detection rate could be achieved offline. These results further motivate us to develop a runtime scheme, BARS, to detect spam posts based on these spamming patterns. The experimental results demonstrate the effectiveness and robustness of BARS.