论文标题
通过加权推文的功能在Twitter中检测事件
Event Detection in Twitter by Weighting Tweet's Features
论文作者
论文摘要
近年来，人们花了很多时间在社交网络上。他们使用社交网络作为对个人或公共事件的评论的地方。因此，在这些网络中每天生成和共享大量信息。使用大量信息可以帮助当局对事件进行准确，及时的反应。在这项研究中，调查的社交网络是Twitter。这项研究的主要思想是根据其某些特征在推文之间进行区分。这项研究旨在通过加权三个推文属性来研究事件检测的性能；包括关注者计数，转发计数和用户位置。结果表明，所提出方法中的平均执行时间和事件检测的精度分别提高了27％和31％，而不是基本方法。这项研究的另一个结果是能够在提出的方法中检测所有事件（包括热门事件和不太重要的事件）。
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.
