Suchergebnisse

Twitter, a microblogging platform, receives real-time information via informal conversations, and it has, accordingly, become the main source of data for research studies based on emergency situational awareness. Millions of tweets are posted on Twitter every day, and during disasters, the frequency of tweets relating to an on-going crisis event grows exponentially. This unprecedented increase in the number of tweets during disasters needs to be monitored, identified, processed, and analyzed so that necessary measures can be taken at the earliest to reduce the loss or damage during emergencies. However, due to large voluminous data being available during crisis hours, it is almost impossible for a human to perform these tasks in real time. In this regard, a semi-automated AI-based disaster response system for Twitter data is proposed. The proposed disaster response system would be capable of extracting essential situational awareness information related to a disaster and would also be capable of sketching tentative area of critically affected population.

Twitter has become the major source of data for the research community working on the social computing domain. The microblogging site receives millions of tweets every day on its platform. Earlier studies have shown that during any disaster, the frequency of tweets specific to an event grows exponentially, and these tweets, if monitored, processed, and analyzed, can contain actionable information relating to the event. However, during disasters, the number of tweets can be in the hundreds of thousands thereby necessitating the design of a semi-automated artificial intelligence-based system that can extract actionable information based on which steps can be taken for effective disaster response. This paper proposes a Twitter-based disaster response system that uses recurrent nets for training a classifier on a disaster specific tweets dataset. The proposed system would enable timely dissemination of information to various stakeholders so that timely response and proactive measures can be taken in order to reduce the severe consequences of disasters. Experimental results show that the recurrent nets outperform the traditional machine learning algorithms with regard to accuracy in classifying disaster-specific tweets.

During disaster events such as floods, landslides, earthquakes, tsunamis, fire hazards, etc., social media platforms provide easy and timely access to information regarding the ongoing crisis events and thereby become an essential vehicle of information sharing. During such events, great amounts of such socially generated data becomes available, which can be accessed and processed to extract situational awareness insights. These insights, in turn, can be used to enhance the effectiveness and efficiency of disaster response in order to minimize the loss of lives and damage to property. People actively use social platforms like Facebook and Twitter to post information related to crisis events. Further, these platforms provide people the location and safety status of their family and friends during such events. Twitter, the microblogging platform, witnesses thousands of informally written tweets during crisis events, and since it provides high-level APIs to access its near real-time feed, it has become the primary source of data for researchers. It is generally observed that there is an exponential burst in the number of tweets during an ongoing crisis event. This sudden burst makes the task of monitoring, identifying, and processing each tweet virtually impossible for a human. However, such voluminous data can be processed using various machine learning and natural language processing techniques in coordination with a certain level of human interventions. This paper is focused on designing a semi-automated artificial intelligence-based classifier, which can classify the plethora of disaster-related tweets into various categories such as community needs, loss of lives, damage.