oa Artificial Intelligence and Social Media to Aid Disaster Response and Management

Extended AbstractPeople increasingly use social media such as Facebook and Twitter during disasters and emergencies. Research studies have demonstrated the usefulness of social media information for a number of humanitarian relief operations ranging from situational awareness to actionable information extraction. Moreover, the use of social media platforms during sudden-onset disasters could potentially bridge the information scarcity issue, especially in the early hours when few other information sources are available. In this work, we analyzed Twitter content (textual messages and images) posted during the recent devastating hurricanes namely Harvey and Maria. We employed state of the art artificial intelligence techniques to process millions of textual messages and images shared on Twitter to understand the types of information available on social media and how emergency response organizations can leverage this information to aid their relief operations. Furthermore, we employed deep neural networks techniques to analyze the imagery content to assess the severity of damage shown in the images. Damage severity assessment is one of the core tasks for many humanitarian organization.To perform data collection and analysis, we employed our Artificial Intelligence for Digital Response (AIDR) technology. AIDR combines human computation and machine learning techniques to train machine learning models specialized to fulfill specific information needs of humanitarian organizations. Many humanitarian organizations such as UN OCHA, UNICEF have used the AIDR technology during many major disasters in the past including the 2015 Nepal earthquake, the 2014 typhoon Hagupit and typhoon Ruby, among others. Next, we provide a brief overview of our analysis during the two aforementioned hurricanes.Hurricane Harvey Case StudyHurricane Harvey was an extremely devastating storm that made landfall to Port Aransas and Port O'Connor, Texas, in the United States on August 24-25, 2017. We collected and analyzed around 4 million Twitter messages to determine how many of these messages are, for example, reporting some kind of infrastructure damage, or reports of injured or dead people, missing or found people, displacements and evacuation, donation and volunteers reports. Furthermore, we also analyzed geotagged tweets to determine the types of information originate from the disaster-hit areas compared to neighboring areas. For instance, we generated maps of different cities in the US in and around the hurricane hit areas. Figure 1 shows the map of geotagged tweets reporting different types of useful information from Florida, USA. According to the results obtained from the AIDR classifiers, both caution and advice and sympathy and support categories are more prominent than other informational categories such as donation and volunteering. In addition to the textual content processing of the collected tweets, we perform automatic image processing to collect and analyze imagery content posted on Twitter during Hurricane Harvey. For this purpose, we employ state-of-the-art deep learning techniques. One of the classifiers deployed in this case was the damage-level assessment. The damage-level assessment task aims to predict the level of damage in one out of three damage levels i.e., SEVERE damage, MILD damage, and NO damage. Our analysis revealed that most of the images (∼86%) do not contain any damage signs or considered irrelevant containing advertisements, cartoons, banners, and other irrelevant content. Of the remaining set, 10% of the images contain MILD damage, and only ∼4% of them show SEVERE damage. However, finding these 10% (MILD) or 4% (SEVERE) useful images is like finding a needle in a giant haystack. Artificial intelligence techniques such as employed by the AIDR platform are hugely useful to overcome such information overload issues and help decision-makers to process large amounts of data in a timely manner.Fig. 1: Geotagged tweets from Florida, USA.Hurricane Maria Case StudyAn even more devastating hurricane than the Harvey that hit Puerto Rico and nearby areas was hurricane Maria. Damaged roofs, uprooted trees, widespread flooding were among the scenes on the path of Hurricane Maria, a Category 5 hurricane that slammed Dominica and Puerto Rico and has caused at least 78 deaths including 30 in Dominica and 34 in Puerto Rico, and many more left without homes, electricity, food, and drinking water.We activated AIDR on September 20, 2017 to collect tweets related to Hurricane Maria. More than 2 million tweets were collected. Figure 2 shows the distribution of the daily tweet counts. To understand what these tweets are about, we applied our tweet text classifier which was originally trained (F1 = 0.64) on more than 30k human-labeled tweets from a number of past disasters. AIDR's image processing pipeline was also activated to identify images that show infrastructure damage due to Hurricane Maria. Around 80k tweets contained images. However, ∼75% of these images were duplicate. The remaining 25% (∼20k) images were automatically classified by the AIDR's damage assessment classifier into three classes as before. Figure 2: Tweets count per dayWe believe that more information can be extracted from image about the devastation caused by the disaster than relying solely on the textual content provided by the users. Even though it is in the testing phase, our image processing pipeline does a decent job in identifying images that show MILD or SEVERE damage. Instead of trying to look at all the images, humanitarian organizations and emergency responders can simply take a look at the retained set of MILD or SEVERE damage images to get a quick sense of the level of destruction incurred by the disaster.