# Use of Social Media Data in Disaster Management: A Survey
## 7 Methodology
### Methodologies Used for Data Analysis
On the other hand, Latent Dirichlet Allocation (LDA), a topic modelling technique in the information retrieval domain, was used in [60]. LDA was used to extract the inherent topic structure from a set of social media messages, and the extracted topic referred to an event (e.g., 2011 Virginia earthquake). The authors gave an example of topics and the proportions of each topic to all messages and showed how the earthquake events captured from the topics constituted a small proportion of messages. Using the LDA topic model approach, meaningful topics with many iterations were discovered. Abnormal events captured from extracted topics did not happen frequently and covered only a small fraction of the social media data stream. In order to identify such abnormal events, the authors used seasonal trend decomposition based on locally weighted regression (Loess), which they called STL. In STL, the reminder component is used to implement control charts. The detected anomaly events are compared with other social media data to confirm the anomalies. A candidate retrieval algorithm was used in [61] for retrieving events from the database. The authors implemented feature extraction to extract spatial, temporal, and textual information, and then used scoring and ranking to determine which document belonged to which event. SVM-based classification was the methodology used in this paper for event detection. An architecture for a public health surveillance process using SMART-C was presented in [63]. The architecture explains the data sources with their modalities, users, and services provided by the underlying system to enable enhanced situational awareness and informed decision-making during all phases of disaster management. The authors discussed the requirements for implementing the following services: event classification/grouping, semantic reasoning, location determination, event extraction, speech analysis, text analysis, video analysis, sensor analysis, geospatial analysis, response planning and generation, and alert dissemination service. They also presented a discussion on security and privacy, event detection, and correlation. Classification and information extraction from Twitter were carried out in [30]. The authors used free part-of-speech-tagging software for Twitter and Weka data mining tools. For classification purposes, they first broadly classified the tweets into personal, informative, and other tweets. They further classified the informative tweets into: (i) caution and advice, (ii) damage, (iii) donations, (iv) people, and (v) other. They used a Naive Bayesian classifier for feature extraction and used unigram, bigram, and part-of-speech (POS) tagging to provide a rich set of features in the classifier. Once a tweet was classified, a sequence labelling task identified relevant information using conditional random fields. A participatory sensing-based model for mining spatial information of urban emergency events was discussed in [64]. The researchers conducted simulations on a typhoon event, Typhoon Chan-hom. They proposed a hierarchical data model with three different layers: a (i) social user layer, (ii) crowdsourcing layer, and (iii) spatial information layer. In the social user layer, the proposed method collected data related to emergency events. In the crowdsourcing layer, the positive samples were collected, and the address and Geographic Information Systems (GIS) data is mined. Information related to the same emergency events was clustered in this layer. In the spatial information layer, the spatial information of the emergency event was mined. Semantic analysis of the geo-tagged microblog data helped obtain a public opinion from the spatial perspective, and assistance could be offered where it was required. From the collected data, it was observed that the risk was high in Beijing, Zhejiang, Jiangsu, and Shanghai. In [34], the authors proposed a probabilistic framework for identifying the location of a Twitter user based on the content of their tweet.