In Twitter based applications such as tweet summarization, the existence of ill-intentioned users so-called spammers imposes challenges to maintain high performance level in those applications. Conventional social spammer/spam detection methods require significant and unavoidable processing time, extending to months for treating large collections of tweets. Moreover, these methods are completely dependent on supervised learning approach to produce classification models, raising the need for ground truth data-set. In this paper, we design an unsupervised language model based method that performs collaboration with other social networks to detect spam tweets in large-scale topics (e.g. hashtags). We experiment our method on filtering more than 6 million tweets posted in 100 trending topics where Facebook social network is accounted in the collaboration. Experiments demonstrate highly competitive efficiency in regards to processing time and classification performance, compared to conventional spam tweet detection methods.