Measuring the Semantic Uncertainty of News Events for Evolution Potential Estimation
Measuring the Semantic Uncertainty of News Events for Evolution Potential Estimation
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DOI：
10.1145/2903719
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Xiangfeng Luo;Junyu Xuan;Jie Lu;Guangquan Zhang
中科院分区：
文献类型：
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作者：
Xiangfeng Luo;Junyu Xuan;Jie Lu;Guangquan Zhang
对新闻事件的演化势进行估计，可以为企业和政府的决策提供支持。例如，如果一个公司事先知道一个关于该公司的负面新闻事件具有很大的演变潜力，那么该公司就可以及时地管理其公共关系危机。然而，现有的方法主要是基于时间序列的历史数据，这是不适合的新闻事件的历史数据有限，突发性的属性。在这篇文章中，我们提出了一个纯粹基于内容的方法来估计新闻事件的演变潜力。该方法将某个时间点的新闻事件看作由不同关键词组成的系统，将该系统的不确定性定义为该新闻事件的语义不确定性。同时，构造了一个包含两个极端状态的不确定性空间：最不确定状态和最确定状态。我们认为，语义不确定性与新闻事件的内容演变具有相关性，因此可以用来估计新闻事件的演变潜力。为了验证所提出的方法，我们提出了详细的实验设置和结果测量的相关性的语义不确定性与新闻事件的内容变化，使用收集的新闻事件数据。结果表明，相关性确实存在，并且比基于时间序列的方法的值与含量变化的相关性更强。因此，我们可以利用语义不确定性来估计新闻事件的演变潜力。
The evolution potential estimation of news events can support the decision making of both corporations and governments. For example, a corporation could manage its public relations crisis in a timely manner if a negative news event about this corporation is known with large evolution potential in advance. However, existing state-of-the-art methods are mainly based on time series historical data, which are not suitable for the news events with limited historical data and bursty properties. In this article, we propose a purely content-based method to estimate the evolution potential of the news events. The proposed method considers a news event at a given time point as a system composed of different keywords, and the uncertainty of this system is defined and measured as the Semantic Uncertainty of this news event. At the same time, an uncertainty space is constructed with two extreme states: the most uncertain state and the most certain state. We believe that the Semantic Uncertainty has correlation with the content evolution of the news events, so it can be used to estimate the evolution potential of the news events. In order to verify the proposed method, we present detailed experimental setups and results measuring the correlation of the Semantic Uncertainty with the Content Change of news events using collected news events data. The results show that the correlation does exist and is stronger than the correlation of value from the time-series-based method with the Content Change. Therefore, we can use the Semantic Uncertainty to estimate the evolution potential of news events.