  In recent years, people spend a lot of 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.
Using such a massive amount of information can help authorities to react to
events accurately and timely. In this study, the social network investigated is
Twitter. The main idea of this research is to differentiate among tweets based
on some of their features. This study aimed at investigating the performance of
event detection by weighting three attributes of tweets; including the
followers count, the retweets count, and the user location. The results show
that the average execution time and the precision of event detection in the
presented method improved 27% and 31%, respectively, than the base method.
Another result of this research is the ability to detect all events (including
hot events and less important ones) in the presented method.
