Social network data, utilised as a VGI source, was analysed using the September 2016 flood event in Messinia, South Greece. The flood event led to damage in the urban and rural environment in the general area, and to human deaths. An innovative methodology is based on applying machine learning to classify Twitter content. Tweets were classified into the following ten categories: (i) flood identification, (ii) rain identification, (iii) consequences of the flood, (iv) expressed emotions, (v) ironic attitude to local disaster management authorities, (vi) disaster management information, (vii); volunteer actions, (viii); situation overview, (ix); social effects, and (x); weather information. Some of the categories were divided further, to quantify significant information. The classified output was sequentially geo-referenced by identifying geographic entities within the text of each post (geo-parsing) and replicating each post according to the number of geolocations. The data processing involved various geo-validations and performance metrics. The final output was used to create maps and graphs of different time periods, that provide useful insights into the flood event for disaster management purposes. The applied methodology is an evolution of previous research published by the author, this time providing complete results, based on the analysis of 100 % of the data available, with maps and graphs which demonstrate how the flood event unfolded in different time periods. The methodology is fully automated in terms of data processing, and can be applied using a script developed by the author in the R programming language. This research is a step towards the real-time delivery of advanced information for all disaster management stakeholders, from official authorities and rescue teams, to volunteers and locals who may be situated within the area of a disastrous flood occurrence.

使用2016年9月在希腊南部墨西拿的洪水事件对用作VGI来源的社交网络数据进行了分析。洪水事件导致整个地区的城市和农村环境遭到破坏，并造成人命死亡。一种创新的方法基于应用机器学习对Twitter内容进行分类。推文分为以下十类：（i）洪水识别，（ii）降雨识别，（iii）洪水的后果，（iv）表达的情绪，（v）对地方灾难管理当局的讽刺态度，（vi）灾难管理信息，（vii）；自愿行动，（viii）；情况概述，（ix）；社会影响，以及（x）；天气信息。对某些类别进行了进一步划分，以量化重要信息。通过在每个帖子的文本中标识地理实体（地理解析）并根据地理位置的数量复制每个帖子，对分类的输出进行顺序地理参考。数据处理涉及各种地理验证和性能指标。最终输出用于创建不同时间段的地图和图表，从而为灾难管理目的提供有用的洪水事件见解。应用的方法是作者先前发表的研究的发展，这次是在对100％可用数据进行分析的基础上，提供完整的结果，并提供地图和图表来演示洪水事件在不同时间段内如何展开。该方法在数据处理方面是完全自动化的，并可以使用作者使用R编程语言开发的脚本来应用。这项研究是朝着为所有灾难管理利益相关者实时提供高级信息迈出的一步，从官方机构和救援团队到可能位于灾难性洪灾发生地区的志愿者和当地人。