In recent years, people spend much time on social networks. They use social networks as a place to comment on personal or public events. Thus, a large amount of information is generated and shared daily in these networks. Such a massive amount of information help authorities to accurately and timely monitor and react to events. This unique specification prevents further damages, especially when a crisis occurs. Thus, event detection is attracting considerable interest among social networks research. Since Twitter is one of the most popular social networks that potentially prepare an appropriate bed for event detection, this study has been conducted on Twitter. The main idea of this research is to differentiate among tweets based on some of their features. For this purpose, the proposed methodology applies weights to the three features, including the followers' count, the retweets count, and the user location. The event detection performance is evaluated by scoring potential clusters based on weighting the three mentioned features. The results show that the average execution time and the precision of event detection in the proposed approach have been improved by 27% and 31%, respectively, in comparison to the base method. Another result of this research is detecting more events (including hot events and less important ones) in the presented method.