# Use of Social Media Data in Disaster Management: A Survey
## 2 Background  
During natural disasters, social media can play an essential role in the emergency response and provide a complete picture of situational awareness during and after the disaster. There are several challenges in acquiring and extracting hazard-related information from social media, including volume, unstructured data sources, signal-to-noise ratio, ungrammatical and multilingual data, and fraudulent message identification and removal. The massive amounts and variety of data generated by social media lead to different levels of information being extracted from the social media data. For instance, geographical information (geo-tagging) attached to a tweet about a roadblock on a hilly road provides more useful contextual information than a similar tweet without geo-tagging. Similarly, a tweet with attached images could potentially provide more situational awareness. For example, a tweet with photos of a roadblock on a hilly road can help people who are driving on the road nearby to understand the current situation of the roadblock and change to a new route away from the blocked area.  
Due to the volume and complexity in such large amounts of social media data, it is crucial to have tools and systems that can automatically classify and extract information, which could turn data into meaningful, actionable information for those attempting to manage the situation. This information has to be systematically managed and made available upon request and to be queried based on different query conditions. The main dimensions of a query include the geo-location/geo-fence, keywords and their disambiguations, user type (e.g., government, non-governmental organisations (NGOs), news agencies, public, etc.), and type of message (e.g., warning, news, SOS, request for supplies, or general posts/tweets about an ongoing or impending situation). It is also important for the system to remove common false-positive patterns. For instance, the word \"Landslide\" in a tweet talking about a landslide victory of a sports team could potentially be classified as a tweet about a landslide hazard. To support this, the use of a tool such as an ontology can be applied to yield meaningful information from complex data. For instance, an ontology of landslides would represent the domain of landslide hazards through relevant terms and relationships between them. These relationships provide formal definitions to the domain terms, thereby enabling machines to understand and analyse them. Thus, the knowledge represented in an ontology enables machines to perform intelligent tasks, such as interactively communicating with social media users to extract contextual information related to an event of interest, identifying the relation of this information with the hazard of interest, and providing this to the decision-maker as a complete picture to enable informed decision-making.  
An ontology-based approach is thus more sophisticated than traditional data management approaches, since it combines the data model with the associated domain knowledge that can be processed by machines to obtain semantically rich and meaningful information [12]. Systematic extraction of important information and semantic meaning from the free text in social media will help make the systems intelligent enough to organise and present data in an actionable form. Similarly, natural language processing (NLP) is an important technology for understanding and extracting information from user-generated text content. We reviewed several natural language processing methods and case studies [13; 14; 15].  
In this survey, the applications described above, including ontological support, NLP, and data mining, are reviewed in the context of social media and natural hazard response and recovery.