School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China.
Int J Environ Res Public Health. 2021 Jul 28;18(15):8000. doi: 10.3390/ijerph18158000.
Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages is studied. First, a case analysis method is used to analyze microblog information after earthquake events. An earthquake emergency information classification hierarchy is constructed based on public demand. Then, subject sets of different granularities of earthquake emergency information classification are generated through the classification hierarchy. A detection model of new topic-words is studied to improve and perfect the sets of topic-words. Furthermore, the validity, timeliness, and completeness of the topic-words detection model are verified using 2201 messages obtained after the 2014 Ludian earthquake. The results show that the information acquisition time of the model is short. The validity of the whole set is 96.96%, and the average and maximum validity of single words are 78% and 100%, respectively. In the Ludian and Jiuzhaigou earthquake cases, new topic-words added to different earthquakes only reach single digits in validity. Therefore, the experiments show that the proposed model can quickly obtain effective and pertinent information after an earthquake, and the complete performance of the earthquake emergency information classification hierarchy can meet the needs of other earthquake emergencies.
社交媒体数据不断更新，数量众多，且具有明显的突出特点。为了从数据中快速提取应对地震紧急情况所需的信息，研究了一种地震应急微博消息的主题词检测模型。首先，采用案例分析方法对地震事件后的微博信息进行分析，根据公众需求构建地震应急信息分类层次结构，然后通过分类层次结构生成不同粒度的地震应急信息分类主题集。研究了一种新主题词的检测模型，以改进和完善主题词集。最后，利用 2014 年鲁甸地震后获取的 2201 条消息，验证了主题词检测模型的有效性、及时性和完整性。结果表明，该模型的信息采集时间短，整体有效性为 96.96%，单个单词的平均和最大有效性分别为 78%和 100%。在鲁甸和九寨沟地震案例中，不同地震新增的主题词有效性仅达到个位数。因此，实验表明，该模型可以在地震后快速获取有效和相关的信息，且地震应急信息分类层次结构的完备性能能够满足其他地震应急的需求。
