Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> Social media is becoming popular as a key source of information on disasters and crisis situations. Humanitarian Aid and Disaster Relief (HADR) respondents can gain valuable insights and situational awareness by monitoring social mediabased feeds. Specifically, the use of microblogs (i.e., Twitter) has been shown to provide new information not otherwise attainable. In this paper, we present a new application designed to help HADR relief organizations to track, analyze, and monitor tweets. The purpose of this tool is to help these first responders gain situational awareness immediately after a disaster or crisis. The tool is capable of monitoring and analyzing location and keyword specific Tweets with nearreal- time trending, data reduction, historical review, and integrated data mining tools. In this paper, we discuss the utility of this tool through a case study on tweets related to the Cholera crisis in Haiti. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> Online social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information.We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves. <s> BIB003 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/. <s> BIB004 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> The overflow of information generated during disasters can be as paralyzing to humanitarian response as the lack of information. Making sense of this information--Big Data--is proving an impossible challenge for traditional humanitarian organizations, which is precisely why they're turning to Digital Humanitarians. This new humanitarians mobilize online to make sense of vast volumes of data--social media and text messages; satellite and aerial imagery--in direct support of relief efforts worldwide. How? They craft ingenious crowdsourcing solutions with trail-blazing insights from artificial intelligence. This book charts the spectacular rise of Digital Humanitarians, highlighting how their humanity coupled with innovative Big Data solutions is changing humanitarian relief for forever. Praise for the book: ...examines how new uses of technology and vast quantities of digital data are transforming the way societies prepare for, respond to, cope with, and ultimately understand humanitarian disasters. --Dr. Enzo Bollettino, Executive Director, The Harvard Humanitarian Initiative, Harvard University ...explains the strengths and potential weaknesses of using big data and crowdsourced analytics in crisis situations. It is at once a deeply personal and intellectually satisfying book.--Professor Steven Livingston, Professor of Media & Public and International Affairs, Elliott School of International Affairs, George Washington University <s> BIB005 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> INTRODUCTION <s> Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization . Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief. <s> BIB006
Social media is a new way of communication in the course of disasters. A major difference between social media and traditional sources is the possibility of receiving feedback from the affected people. Responders such as Red Cross can benefit this two-way communication channel to inform people and also gain insight by monitoring their posts. Twenty million tweets after Hurricane Sandy (2012) and eight million tweets after the Boston Marathon Bombings (2013) BIB003 have been published on Twitter. This swarm of posts can provide valuable insight and help with the disaster management when the functioning of a community is disrupted due to severe fatalities and infrastructural damage [1; 12] . Two types of insight can be obtained from social media in the course of disasters. The "big picture" is an estimate of the scope of the disaster: area, casualties, and failed structures. "Insightful information" is more detailed and is available when more data is available on social media. Locations that need food, medical supplies, or blankets are examples of insightful information  . One challenge associated with acquiring insight via social media is processing enormous amount of information in a timely manner. After Japan earthquake and tsunami (2011), 1,200 tweets were published every minute from Tokyo  and after Hurricane Sandy (2012), the peak rate of 16,000 tweets per minute has been reported BIB005 . This amount of data is too large to be manually processed by emergency responders. Some of the proposed methods to overcome this issue are presented in Section 3.1. Another challenge is the wide-spread of unwanted content such as daily chatter, spam, and rumor in social media. Among the 8 million tweets related to Boston Marathon Bombings (2013), 29% were found to be rumors and 51% to be generic opinions and comments BIB003 . Moreover, exploiting bots has worsened this issue. Large number of bots can be generated in a short period of time and be used to spread spam, deviate the conversation of real users, and help with the virality of rumors. We mention some of the solutions to this challenge in Section 3.2. Disasters have eight socio-temporal stages: Pre-disaster, Warning, Threat, Impact, Inventory, Rescue, Remedy, and Recovery  . The volume of social media posts varies in each stage; majority of users start posting after the disaster onsets and the frequency decreases when the disaster reaches its final stages. Availability of data is a major factor in building automatic methods for facilitating the management tasks. Hence, we consider four stages in disasters; the ones in which social media posts are dense enough for Machine Learning methods to achieve reliable results: Warning, Impact, Response, and Recovery. In the warning stage (Section 4), social media can be used as a complementary source of information to help increase the confidence in predicting disasters and providing warnings. Changes in the frequency of posts with specific words and topics, activity patterns of users [5; 83; 92] , and sentiment of posts BIB001 are used to predict disasters. Predicting disasters before they hit an area provides the opportunity to warn people in danger and evacuate elevators and operation rooms. Currently, USGS uses tweets to check the accuracy of sensor reports and detect earthquakes in a shorter time. Earthquakes can be detected using tweets by 60 seconds earlier than sensors; this time is valuable for warning areas in danger and starting evacuation processes  . When disasters impact an area, social media posts show anomalies such as changes in the language. A study  on LiveJournal after 11 September 2001 shows that emotional positivity decreased and cognitive processing, social orientation, and psychological distancing increased after the attack BIB006 . These changes in social media posts can be quantitatively captured in sentence level or topic level. Capturing the change in real-time results in detecting disasters before they are announced by official sources, governmental websites, or major news outlets  (see Section 5) . In response to the chaotic environment caused by disasters, emergency responders want to acquire actionable insight and a big picture of the disaster  . Detecting and tracking topics, trends, and memes on social media provides information regarding the status of disasters and the affected people. Damages, casualties, missing animals, and failed structures are some of the topics that people discuss on social media. Tracking these topics, discovering the trends, and monitoring mentioned locations help responder distribute resources more efficiently (see Section 6) . Volunteers are significantly important in the relief process. They post information that increases situational awareness (e.g. status of roads and damages to built structures) and provide technical support for translating social media posts and geotagging them. Some of the systems that exploit social media posts to facilitate disaster management are Ushahidi  , AIDR BIB004 , and TweetTracker BIB002 which will be discussed in Section 7 with more details. In this paper, we clarify the relation between the stages of disasters and relevant research on social media. This effort is towards unwrapping the potentials of social media to be exploited by emergency responders to a larger extent. We consider four stages for disasters which are widely reflected on social media: warning, impact, response, and relief. In each stage, we introduce approaches that use social media to ease the relief efforts. The major difference between this work and previous surveys in the field is the organization of the material in an effort toward facilitating the exploitation of these methods by disaster responders. We use disaster management stages used by first responders  to explain limitations and potentials of social media research. We bold available methods and tools that can be used by responders and mention the areas in which social media has not been used to its potential. Moreover, we focus on more recent areas which are not widely reflected in previous studies. We believe that this work establishes a connection between available tools based on social media data and the efforts of first responders. Contributions of this paper are as follows: • Introducing four stages for disasters based on activities of users on social media. • Categorizing research on social media for disaster management based on their application in each stage. • Connecting the research on social media with disaster management efforts by first responders. • Including recent studies on social media in this area that have not been included in similar efforts.