With the increasing popularity of online social networking platforms, the amount of social data has grown exponentially. Social data analysis is essential as spamming activities and spammers are escalating over online social networking platforms. This paper focuses on spammer detection on the Twitter social networking platform. Although existing researchers have developed numerous machine learning methods to detect spammers, these methods are inefficient for appropriately detecting spammers on Twitter due to the imbalance of spam and nonspam data distribution, the involvement of diverse features and the applicability of data mechanisms by spammers to avoid their detection. This research work proposes a novel hybrid approach of the gravitational search algorithm and the decision tree (HGSDT) for detecting Twitter spammers. The individual decision tree (DT) algorithm is not able to address...