# Use of Social Media Data in Disaster Management: A Survey
## 7 Methodology
### Methodologies Used for Data Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{2}{c}{**Methodology**} & \multirow{2}{*}{**Pros and Cons**} \\ \cline{2-3} \cline{5-5}  & **Data Management** & & **Data Analysis** \\ \hline \multirow{9}{*}{[15, 58]} & GATE Mimir (Multi-paradigm & Three cases of tweet studies: 2015 & \\  & Information Management & UK election, EU referendum, & \\  & Index and Repository & and political reactions & \\  & was used for indexing and & to climate change. & \\  & querying. GATE Cloud & Semantic analytics pipeline & The proposed system is not \\  & Paralleliser was used for & with Named Entity & domain-specific, and the study \\  & non-live processing. & Recognition (NER) & was done using Twitter data \\  & For real-time processing, a message- & (using TweetIE) and & only. Both data management \\  & queueing-based system & Named Entity Linking (NEI) & and data analysis methodologies \\  & was used. & (NEL) (using YODIE). & depend on the GATE framework. \\  & & Topic detection from & \\  & tweets and sentiment analysis. & & \\ \hline \multirow{9}{*}{[50]} & Consists of a pipeline of tools to geotag, classify text, and tag & CrisisNLP and CrisisLexT26 & Geo-locating tweets using OpenStreetMap and location mentions. Matching locations with “Needs” to \\  & \"Needs” from tweets. & & \\  & Elastic search is used & D-Record ontology was used, which consists of the concepts of “Needs” and “Availability”. & \\  & data. Data sources consist of Twitter, OpenStreetMap, and satellite images. & Support vector machine (SVM)-based text classifier. & Relatively smaller set of data. \\ \hline \multirow{9}{*}{[59]} & Semantic analyses were used to extract tweets & Used a support vector machine (SVM) for the tweet classification and a probabilistic model for location estimation. & An example of real-time earthquake event detection using semantic analysis and a real-time Twitter feed where a user functions as a sensor. The precision and recall of the system based on numbers of tweets were evaluated to choose the best performance. The model assumes a single instance of a target event. & An example of real-time earthquake event detection using semantic analysis and a real-time \\ \cline{1-1}  & Semantic analyses were used to extract tweets & Used a support vector machine (SVM) for the tweet classification and a probabilistic model for location estimation. & Twitter feed where a user functions as a sensor. The precision and recall of the system based on numbers of tweets were evaluated to choose the best performance. The model assumes a single instance of a target event. & Twitter feed where a user function as a sensor. The precision and recall of the system based on numbers of tweets were evaluated to choose the best performance. The model assumes a single instance of a target event. & As a sensor. The precision and recall of the system based on numbers of tweets were evaluated to choose the best performance. The model assumes a single instance of a target event. & The abnormal event detection was based on probabilistic topic extraction and time-series decomposition. Interactive visual analytic system. Analytics and processing approach were not of a real-time nature. & The abnormal event detection was based on probability topic extraction and time-series decomposition. Interactive visual analytic system. Analytics and processing approach were not of a real-time nature. & Precision, recall, and F-measure for evaluation. The system makes use of multiple social media sources, which is an advantage. The event candidate retrieval requires historical analysis. The system uses a traditional SQL database, which is not scalable for large social media datasets. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Methodologies. events were classified as a positive class, while tweets unrelated to earthquake events were classified as a negative class. Furthermore, they used a machine learning algorithm, support vector machine (SVM), for tweet classification.