Event Detection in Twitter by Weighting Tweet's Features
This work addresses event detection for authorities using social media data, but it is incremental as it builds on existing methods with feature weighting.
The study tackled event detection in Twitter by weighting tweet features like followers count, retweets count, and user location, resulting in a 27% improvement in average execution time and a 31% increase in precision compared to the base method, while also enabling detection of all events including less important ones.
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.