加权推文特征的推特事件检测

Parinaz Rahimizadeh, M. Shayegan
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引用次数: 0

摘要

近年来,人们在社交网络上花费了很多时间。他们利用社交网络对个人或公共事件发表评论。因此,在这些网络中每天产生和共享大量的信息。如此大量的信息有助于当局准确及时地监测和应对事件。这种独特的规范可以防止进一步的损害,特别是在发生危机时。因此,事件检测在社会网络研究中引起了相当大的兴趣。由于Twitter是最受欢迎的社交网络之一,可能为事件检测提供了合适的温床,因此这项研究是在Twitter上进行的。这项研究的主要思想是根据推文的一些特征来区分推文。为此,提出的方法对三个特征应用权重,包括关注者数量、转发数量和用户位置。事件检测性能通过基于上述三个特征的加权对潜在聚类进行评分来评估。结果表明,该方法的平均执行时间和事件检测精度分别比基本方法提高了27%和31%。本研究的另一个结果是在本方法中检测到更多的事件(包括热点事件和次要事件)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Event Detection in Twitter by Weighting Tweet’s Features
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.
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