# Use of Social Media Data in Disaster Management: A Survey
## 6 Information Dimension
### Temporal  
Most social media applications attach a time stamp to the posted data. The temporal relation between events can be derived from the time stamp of the event and the content. We studied how temporal information is used in the existing systems for event detection. In the context of event detection, we categorised the temporal information into three categories as shown in Figure 5.  
* Pre-event: This represents the time period before the occurrence of the event of interest. In general, a social media message that is posted before the event occurrence can be analysed to derive the following information: (i) warnings--e.g., a post about bad weather from the Met-Office before heavy rainfall, a cyclone alert, etc. serves as a warning message for an impending natural disaster, (ii) precursor event detection--e.g., a social media post about a leaning electric pole in a location can serve as a precursor for landslide event detection, and (iii) temporal offset--pre-event posts from social media are analysed to determine the offset between the time of the post and the time of the actual event, such as, for instance, the time taken after the leaning pole post and the actual landslide in that locality. Pre-event posts from social media can thus be utilised for serving the mitigation and preparedness phases of emergency management. * Real-time: This represents the time span during which the event is happening. In the real time of the event occurrence, social media may be widely used for information sharing about the incidents related to the event. Generally, the real-time posts from social media during the occurrence of the event can be analysed for: (i) obtaining situational awareness--e.g., \"trains cancelled, schools closed in Kerala due to heavy rains\", or a social media post about \"roadblock due to landslides on NH-8\", (ii) deriving/issuing warnings about the after-effects/impacts of a disaster--e.g., \"high  
Figure 5: Temporal. tides are expected in coastal areas after the tremors\", and (iii) response, relief, and recovery--e.g., a tweet during the Kerala flood in 2018: \"shortage of bubble wrap and ready-to-eat items in Sanskrit College Palayam\". * Post-event: This represents the time period after the occurrence of the event of interest. Often, after disasters, social media is widely used to communicate about required supplies, information about missing people, death tolls, property losses, relief operations planned by the government and NGOs, protective measures to be undertaken while returning home, funds donated by various authorities, etc. Thus, the post-event data can generally be analysed for: (i) warnings of further events, (ii) deriving information on the impact of the event, (iii) identifying the relief and recovery measures required, and (iv) determining the temporal offset between the time of the post and time of the actual event. It is important to analyse the behaviour of the public/communities before, during, and after disasters in order to bring in effective disaster response, management, planning, and mitigation. Since social networks serve as the easiest and most common way to sample public opinion, we can make use of the time-stamped, geo-tagged data from social media for this purpose. Table 5 summarises the temporal the state-of-the-art methods for temporal information analysis. [PERSON] et al. [39] explain the temporal analysis of Twitter data related to hurricane Sandy, wherein they analyse the Twitter user density distribution two weeks before and after the date of the event, as well as for a time period on the day of the event, right after the announcement of the evacuation order. A similar study on the spatiotemporal analysis of Twitter data for the same disaster event was performed by [PERSON] et al. [40], according to whom the persistence of the Twitter activity levels in the time frame immediate to the occurrence of the event (post-event) was a good indicator for determining which areas were likely to need the most assistance.