Abstract:

Aiming at the problem that the existing microblog event extraction methods are based on the content characteristics of events and ignore the relationship between the social attributes and time characteristics of events， so that they cannot identify the key events in the propagation process of microblog hot spots， a key event extraction method from microblog by integrating social influence and temporal distribution was proposed. Firstly， the social influence was modeled to present importance of microblog events. Secondly， the temporal characteristics of microblog events during evolution were considered to capture the differences of events under different temporal distributions. Finally， the key microblog events were extracted under different temporal distributions. Experimental results on real datasets show that the proposed method can effectively extract key events in microblog hot spots. Compared with four methods of random selection， Term Frequency-Inverse Document Frequency （TF-IDF）， minimum-weight connected dominating set and degree and clustering coefficient information， the proposed method has the event set integrity index improved by 21%， 18%， 26% and 30% on dataset 1 respectively， and 14%， 2%， 21% and 23% on dataset 2 respectively. The extraction effect of the proposed method is better than those of the traditional methods.

Key words: social influence, temporal distribution, microblog, event extraction, event evolution

摘要：

针对现有微博事件抽取方法由于基于事件的内容特征，而忽略事件本身的社会属性与时间特征之间的关系，进而无法识别微博热点传播过程中关键事件的问题，提出了一种融合社会影响力和时间分布的微博关键事件抽取方法。首先通过建模社会影响力来刻画微博事件的重要性，然后融合微博事件演化过程中的时间特性以捕获事件在不同时间分布下的差异，最后抽取出不同时间分布下的微博关键事件。在真实数据集上的实验结果表明，所提方法能有效抽取微博热点中的关键事件，较随机选择、词频-逆文本频率（TF-IDF）、最小权重支配集以及度与聚集系数这四种方法在事件集的完整性指标ROUGE-1上在数据集1上分别提升了21%、18%、26%以及30%，在数据集2上分别提升了14%、2%、21%以及23%，抽取效果优于传统方法。

关键词: 社会影响力, 时间分布, 微博, 事件抽取, 事件演化