Social network sites such as Facebook, Twitter, YouTube, etc., are very popular among internet users for information sharing and communication. This popularity also attracts cybercriminals to spread their malicious activities including spams using social networks. Spam may contain unsolicited information or malicious links that may harm Twitter users and make them feel insecure. So there is a crucial need for effective spam detection which requires the distinctive features that clearly portray the spammer behavior. This is accomplished by information theoretic-based feature selection methods, as these methods select the informative features that retain the original meaning. Most of the existing information theory-based feature selection methods focus on retaining the features that contain more information while removing features that contain less information for improving the performance. This may lead to the information loss. Also, the features that are individually less significant may be useful when combined with other features. So, Community Inspired Firefly Algorithm for Spam detection (CIFAS) is proposed to handle combination search for the features that provide good performance using fuzzy cross entropy as the fitness function. Fuzzy cross-entropy is used in this work, to preserve the information contained in the selected feature subset equally to the information contained in full feature set. From the experimental result, it has been proven that the proposed CIFAS offers defense against spam similar to the original feature set and performs better than the existing approaches in terms of accuracy, false-positive rate, and F measure for the two standard twitter datasets.