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ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering

机译:ScatterBlogs2:通过用户指导的过滤实时监视微博消息

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The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.
机译:每天发布的微博帖子数量已达到阻碍有效检索相关消息的水平,并且通过Twitter等服务传递的信息量仍在增加。分析师需要新的方法来监视他们感兴趣的话题,处理数据量及其动态性质。为时间紧迫的任务中的决策提供态势感知尤其重要。当前用于监视微博的工具通常会根据用户定义的关键字查询和元数据限制来过滤消息。单独使用时,此类方法在过滤器准确性以及对趋势和主题结构的更改的适应性方面可能存在缺点。我们建议使用ScatterBlogs2,这是一种新方法,可让分析人员基于已充分理解的先前事件的记录消息,以交互和可视的方式构建任务定制的消息过滤器。这些消息过滤器包括受监督的分类和查询创建,这些查询由术语及其同现的统计分布支持。可以对创建的过滤器方法进行编排和调整,以用于交互式,视觉实时监视和分析微博提要。我们演示了在紧急管理方案中分析Twitter流的方法的可行性。

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