# Use of Social Media Data in Disaster Management: A Survey
## 7 Methodology
### Methodologies Used for Data Management  
Data management for social media includes collecting, indexing, storing, and querying social media data for the accessibility, reliability, and timeliness of the data. Social media generates a large volume of data every day. For instance, according to [45], Facebook generates around four petabytes of data every day. The sheer amount of data itself poses a significant challenge in social media data management, making it a Big Data problem. Data management and analysis systems for social media data must, therefore, be able to handle the \"four Vs\" of Big Data analytics--namely, volume, variety, velocity, and veracity. In this section, we present the state of the art in various systems and in research involving social media analytics for disaster management. From the data management perspective, we reviewed how data are collected, filtered, pre-processed, localised, stored, indexed, and queried. [PERSON] et al. [15] presented a framework for real-time semantic social media analysis that is based on a popular open-source framework for natural language processing, GATE [46]. For the evaluation of the framework, they used the Twitter streaming API for data collection. Both streaming and batch processing approaches were evaluated. The GATE Cloud Paralleliser (GCP) [47] was used to perform batch processing of text, as it supports execution of NLP pipelines with millions of documents. It performs the pre-processing and transformations required to load into the main information management system in the GATE pipeline, Mimir (Multi-paradigm Information Management Index and Repository). It also supports indexing of text, annotations, and semantics. In real-time stream analysis, the Twitter client is used to capture data from the Twitter streaming API to feed into a message queue. Separate semantic analysis processors analyse and annotate the text and push it into Mimir, which, in turn, enables semantic search using the  
Figure 6: Methodology. knowledge encoded in knowledge graphs or ontologies [48]. This enables the indexed documents to form semantic relationships and, thus, makes it easy to perform complex semantic searches over the indexed dataset. GATE Prospector [14] is used for exploring and searching datasets in the Mimir system. This system is reviewed in the next section. [PERSON] et al. proposed a conceptual framework [49] for social media data collection and quality assessment. The framework's strategy consists of three major steps to develop, apply, and validate search filters. Retrieval precision and retrieval recall are measured. Quality assessment in data collection is an important aspect of analysing a large amount of data, such as social media content. This is very relevant in the disaster management scenario. Search filter development is performed with keyword selection, which includes disambiguations and slang words, and this procedure was generally performed manually by domain experts. Search filters are developed using standard logical operators, such as AND, OR, and NOT, and by involving data preprocessing techniques, such as n-gram analysis and proximity operators. D-record [50] utilises three data sources: Twitter, OpenStreetMap, and satellite images. A set of keywords for a needed concept was expanded using topic modelling learned using an support vector machine (SVM)-based classifier with Synthetic Minority Over-Sampling Technique (SMOTE). In \"Twitter Analytics: A big data management perspective\" [13], [PERSON] et al. reviewed several open-source and commercial tools for data collection, management, and querying for Twitter, many of which have been used in disaster management applications. [PERSON] et al. (2013) and [PERSON] et al. (2010) [51; 52] developed a scalable Cyber Infrastructure-based Geographic Information Systems (CyberGIS) for analysing large amounts of social media content in a natural disaster context. The system employs data fusion techniques to fuse social media data with census data and remote-sensing imagery. [PERSON] et al.