It contains all the incoming texts, location and pictures from our users.
User Id | Text |
{{item}} | {{document[item]}} |
We use a Machine learning and deep-leaarning model to priortize the texts, and audio from the users, to provide effecient quick remedy.
Image | User Id | Priority Score | Text |
{{item[0]}} | {{item[1]}} | {{document[item[0]]}} |
The inflow of tweets with #EmergencyName, as there are around 5x more tweets during a disaster.
Terai in trouble! Flood everywhere!small help can bring big change in their lives!lets help! @iamsrk u r the reason behind this unity! Thnx! pic.twitter.com/TSeo6H7BDu
— SRK Fan Club NEPAL (@club_nepal) August 12, 2017
#Nepalfloods: Death toll rises to 78; 35,000 houses inundated, thousands displaced https://t.co/4stFAz7xrx
— Firstpost (@firstpost) August 14, 2017
Tweets by DDNewsliveMonsoon 2019 deluge
— Nepali Times (@NepaliTimes) July 13, 2019
The monsoon arrived late, but when it did it burst with vengeance. Eastern #Nepal has got 500mm of #rain since Thursday, Simara received 300mm just on Friday alone, and #Kathmandu got 150mm in 12 hours on Friday.
More: https://t.co/qaLOHIEDM8#NepalFlood pic.twitter.com/lOjO2yHVoV
We use a Operation Research Techniques like Primal and Dual methods to priortize the Tweets.
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