When disasters occur – such as hurricanes, forest fires and earthquakes –Every second counts. Emergency teams must find people quickly, send help and stay organized. In today’s world, one of the fastest ways to get information is social media.
In recent years, researchers have investigated how artificial intelligence can use social media to help during emergency situations. These programs can Scan millions of messages On sites such as X, Facebook and Instagram. However, most existing systems look for simple patterns such as keywords or images of damage.
In my research as a AI scientistI have developed new models that will continue. They can Meaning and context of messages– What researchers call semantics. This helps to improve how accurately the system identifies people in need and classifies information about situational consciousness during emergency situations. The results show that these tools can give save teams a clearer view From what is happening on the ground and where help is most needed.
From messages to life -saving insights
People share Billions Every day on social media. During disasters they often share photos, videos, short messages and even their location. This creates a huge network of real -time information.
But with so many messages it is difficult for people to find what is important quickly. That is where artificial intelligence helps. These systems that use Machine learningcan scan thousands of messages every second, find urgent messages, spot damage in photos are shown and tell real information from rumors.
During Hurricane Sandy in 2012, people Sent more than 20 million tweets for six days. If AI tools were used, they could have helped find people in danger even faster.
Training AIS
Researchers start by Teach ai programs to understand emergency situations. In one study I conducted, I looked at thousands of messages on social media of disasters. I sorted them into groups such as people who ask for help, damaged buildings and general comments. Then I used these examples to train the program To sort new messages in itself.
A big step forward was learning the program to view photos and words together. A photo of flooded streets and a message like “we are trapped” are, for example, stronger signals than one of them alone. With both the system became much better at showing where people needed help and how serious the damage was.
Finding information is only the first step. The main goal is to help emergency teams to act quickly and save lives.
I work with emergency aid teams in the United States Add this technology to their systems. When a disaster strikes, my program can show where help is needed by using messages on social media. It is also possible Classify this information by urgencyHelping to help rescue teams use their resources where they are most needed.
During a flood, for example, my system can quickly see where people ask for help and rank these areas in urgency. This helps to act and steer rescue teams, help where it is most needed, even before official reports arrive.
Tackling the challenges
The use of social media to help during disasters sound great, but it is not always easy. Sometimes people post things that are not true. Other times the same message is often posted or it is not clear where the problem is. This mix can make it difficult for the system to know what is real.
To solve this, I work on ways to check one Post’s credibility. I look at who it posted, which words they used and or other messages say the same.
I also take privacy seriously. I only use messages that someone can see and never show names or personal information. Instead, I look at the big picture to find patterns.
The future of disaster information
As AI systems improve, they are probably even more useful during disasters. New tools can understand messages more clearly and can even help us see where problems are coming before it starts.
As extremely deteriorates, the authorities need fast ways to get good information. When used correctly, social media can show people where help is needed. It can help save lives and get supplies to the right places faster.
In the future I believe that this will be a fixed part of an emergency work around the world. My research is still growing, but one thing is clear: disaster response is no longer just about people in the field – it is also about AI systems in the cloud.
Adecela Adimola is a postdoctoral researcher in computer science Missouri University of Science and Technology.
This article has been re -published from The conversation Under a Creative Commons license. Read the Original article.
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