Edit Use Case: Tornado in Paderborn

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Core data
Extended data
Questions

Category	Exercise Preventive campaign Real-world
Year
Publishing Organisation:
Location City and country.
Hazard e.g., earthquake, flood
Scale Leave at "none" if you don't know the answer.	None/unknown City Country County
What is the overall timeframe of the use case? Leave at "none" if you don't know the answer.	None/unknown Less than 1 day 1 to 3 days More than 3 days
Brief summary Required. This text will also appear in the list of all use cases.	This use case gives a broad overview of the application of a social media technology (Ubermetrics) while a tornado hits the city of Paderborn. The gathering and analysis of information is shown.

Theme Which general theme did the Use Case cover?	Crowdsourcing Social Media
Disaster Management Phase	After Before During
Which vulnerable groups were involved?
Which social media and crowdsourcing technologies were used?
Was a technology specifically developed for this use case? Leave at "none" if you don't know the answer.	None/unknown yes no
Which guidelines were used?
Which Social Media Platforms were used?
Which hashtags or keywords were used (if applicable)? e.g., #covid #tornado

Description If you would like to extend the brief summary, you can do so here.	On the 20th of May 2022, a tornado cut a swath of devastation from west to east across the city over a width of 300 meters. Throughout the district, roofs were torn off, metal insulation and other materials flew for kilometers, the fire department said. Severe damage was reported, over 1000 trees were uprooted, many buildings and cars destroyed. Public transport, including railroads, was severely affected. Overall, 43 people were injured, 13 of them seriously. As usual for such events, citizens produced a lot of information (texts, pictures and videos) and provided on different social media platforms. From the development of a research project with the safety innovation center as the coordinator, the fire department of Paderborn is using INSPIRE for the monitoring and analysis of social media information. The gathered information granted the fire department helpful insights in the management of operations. '''Overview''' The information were mainly gathered from Twitter. The following figure shows the number of social media posts when the tornado hits. '''Early warning''' Past experience shows that the probability of a tornado in Germany is very low. Likewise, it is typical for a tornado that it can only be predicted in the short term. Accordingly, the awareness of the population and authorities for the development of a tornado was not very high. The tornado hit the city at 17:13 and early warnings for an upcoming tornado were posted by so called tornado-spotters (well known in USA). '''Increased Situational Awareness''' The mass of social media posts were filtered using both a hashtag search and a keyword search. This is a collection of keywords that match the selected scenario (for example, in the case of a tornado, these are keywords like storm, severe weather, destruction, etc.). Thus, the available number of social media posts is reduced and filtered for relevant posts, which can then be reviewed by disaster management organisations to expand the situation picture. In the case of a large-scale incident, the fire department of Paderborn had to work through the emergency scenes step by step. Accordingly, the fire department only arrived at the scene of the damage a few hours later or even at night or the next day. The information from social media was relatively easy to assign to an operation site, especially for those familiar with the city. In many cases, this made it possible to view and assess the situation at the scene of the incident even before the firefighters themselves went there. In this way, priorities could be refined and the deployment of forces could be adjusted. '''Event Notification''' Another function that the Paderborn Fire Department uses is the detection of anomalies in observed social networks. This means, for example, a high rate of increase in the number of results of a defined search over a certain period of time. If, for example, there is a 10% increase compared to the last 3 hours, the fire department is automatically notified.
Involved Organisations:
What was the overall goal of the Use Case?	Gather and filter an overwhelming amount of information when a disaster or crisis-related emergency occurs.
Which thematics are closest to the goal of the Use Case?	Collecting and Analysing Information from SMCS Ensuring Credible Information Making Information Accessible Mobilising Citizens Mobilising Volunteers Targeting Communication
What limitations were identified? e.g., regulatory hurdles
What worked well and could be recommended to others?
Links for further reading Important tweets, sources, articles, photos, videos, etc.

Summary:

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