检测当前事件以预测未来:Twitter上事件的检测和演变

Muhammad K. Ali, Lu Liu, Mohsen Farid
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引用次数: 3

摘要

事件不是某件事的一次性发生,而是一连串连续不断的小事件。与事件检测一样,事件演化也同样重要。大多数现有的方法都忽略了事件的演变,进一步无法确定该事件的有影响力的传播者。此外,对于持续发展事件的影响,预测与其他过早事件的联系,将有助于商品和股票市场等经济领域。Twitter被广泛用作有效的数据收集来源,并提供独特的关键字(标签)。然而,它没有提供关于趋势的见解,这使得很难发现事件。本文的研究实验环境和初步结果目前基于英国脱欧的历史数据集。但是,我们计划定义合适的方法,在不同时间同时对整个动态数据集进行快照,最终使用实时数据。我们的研究动机是开发一种新的数据分析系统和支持技术来发现事件,以增强决策,,,,过程。
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Detecting Present Events to Predict Future: Detection and Evolution of Events on Twitter
Event is not a one-off occurrence of something, rather it is a continuous chain of small events. Along with event detection, the event evolution is equally important. Most existing methods ignore the evolution of the events and further fails to identify the influential spreaders of that event. Moreover, for the impact of the continuous developing events, predicting the linkages with other premature events, will help in the domains of economy such as commodities and stock markets. Twitter is widely used as an effective source of data collection and provides unique keywords (hashtags). However, it does not provide insights about the trend which makes it difficult to detect events. The research experiment environment and preliminary results presented in this paper are currently based upon the Brexit’s historical dataset. However, we plan to define proper way to simultaneously snapshot whole dynamic dataset at different times and eventually use real-time data. Our motivation for the research is to develop a new data analytic system and supporting techniques to find events for enhancing the decision-making,,,, process.
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