论文标题
社交媒体中的洪水和热浪通过人工智能响应中的热浪
Behaviour in social media for floods and heat waves in disaster response via Artificial Intelligence
论文作者
论文摘要
本文分析了英国多种与灾难相关的洪水和热浪收集中的社交媒体数据。提出的方法使用基于在灾难响应和极端事件的基准数据集中培训的深层双向神经网络的机器学习分类器。将结果模型应用于文本数据中推断主题的情感和定性分析。我们进一步分析了一组行为指标，并通过解码概要记录来分析热舒适度，并与气候变量匹配。我们强调了对齐行为指标以及气候变量的优势，以提供其他有价值的信息，尤其是在灾难的不同阶段，适用于极端天气时期。消息的积极性约为灾难的8％，灾难和医疗反应1％，灾难和与人道主义有关的信息为7％。这显示了此类数据对我们的案例研究的可靠性。我们显示了将这种方法应用于任何社交媒体数据收集的可转移性。
This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform sentiment and qualitative analysis of inferred topics in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection.
