Intelligent Disaster Response via Social Media Analysis A Survey <s> Rumors <s> In this article we explore the behavior of Twitter users under an emergency situation. In particular, we analyze the activity related to the 2010 earthquake in Chile and characterize Twitter in the hours and days following this disaster. Furthermore, we perform a preliminary study of certain social phenomenons, such as the dissemination of false rumors and confirmed news. We analyze how this information propagated through the Twitter network, with the purpose of assessing the reliability of Twitter as an information source under extreme circumstances. Our analysis shows that the propagation of tweets that correspond to rumors differs from tweets that spread news because rumors tend to be questioned more than news by the Twitter community. This result shows that it is posible to detect rumors by using aggregate analysis on tweets. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Rumors <s> Section I: Social Media, Organizational Engagement, and the Impact of Citizens Introduction to the Application of Social Media in Modern Emergency Management Modern Emergency Management The Rise of Social Media Social Media Users Centralized versus Decentralized Organizational Structure Systems Fade, Concepts Remain Overview of Chapters Practitioner Profile: Jeannette Sutton, PhD, Disaster Sociologist Social Media Systems: Overview and Purpose Foundations of Social Media Social Networks Blogs Microblogs Photo Sharing Video Sharing Video Streaming Skype and Video Calling Other Systems Practitioner Profile: Hal Grieb, Previstar Citizen Journalism: The Rise and Impact of New Media Journalism-Traditional and Participatory Acceptance by the General Public Acceptance by News Media Available Tools of a Citizen Journalist Challenges to Citizen Journalism Practitioner Profile: Tom Erickson, Johnson County Sheriff's Office Mountains or Molehills: Engagement Challenges in the Application of Social Media Hurdles and Hindrances Programmatic Implementation The Challenge of Privacy Policy Implementation Challenges to Implementation Practitioner Profile: Jason Lindesmith, Federal Emergency Management Agency Headquarters The Yellow Tape Conundrum: Citizen and Responder Responsibility A Change in Public Expectations Rise of New Systems Incident-Based Risk First Responder Responsibility Citizen Responsibility Social Media "How" versus Social Media "Why" Practitioner Profile: Alisha Griswold, Medical Reserve Corps, Kansas City Section II: Social Media Policy, Procedure, Integration, and Analysis Who's the Sheriff in These Parts? Monitoring and Analysis of Social Media Information Traditional Media Monitoring Social Media Monitoring Real-Time Instantaneous Monitoring Real-Time Collective Monitoring Basic Monitoring Analytics Measuring Influence and Success Practitioner Profile: Ethan M. Riley, Arizona Division of Emergency Management White Hot or White Noise? Aggregation and Validation of Social Media Information Impact of Demographics Power of Aggregation Theory of Social Validation The Power of the Virtual Voice RSS and Other Aggregation Tools Integration with Traditional Systems Practitioner Profile: Lee Arning, Southern Methodist University When Status Quo Becomes Obsolete: Modern Integrated Emergency Warning and Notification Strategies The Fallacy of Traditional Approaches Mobility and Portability Dynamic and Diverse Emergency Notification Strategies Next Generation 9-1-1 Limitations to Modernization Practitioner Profile: Walt Way, Johnson County Emergency Communications Center Volunteer and Donations Management 2.0: How Social Media Has Revolutionized the Management and Recruitment of People and Supplies Demographics of Volunteerism Contemporary Volunteer Management Voluntweeters and Other Crowdsourcing Opportunities Donations Management 2.0 Measurements of Success Practitioner Profile: Heather Blanchard, Co-Founder of Crisis Commons The Elephant in the Emergency Operations Center: The Fundamental Flaw within Formal Response Systems National Preparedness and Response Systems Conflicts and Contradictions Take the Filter Off Contrasting Opinions Challenges to Social Media and Exercise Management Practitioner Profile: Gerald Baron, Public Relations and Crisis Communications Consultant Section III: Social Media Tools and the Power of Virtual Community It Takes a Village to Raise a Prepared Community: The Power and Purpose of Crowdsourcing What Is Crowdsourcing? How Crowdsourcing Works Witnessing Disaster Crowdsourcing by Virtual Volunteers Organizational Implementation of Crowdsourcing Crowdsourcing by Impacted Citizens Crowdsourcing Usage by Governmental Response Practitioner Profile: Jim Garrow, Philadelphia Department of Public Health The Beaten Browser: The Rise of Video, Voice, and Information on the Go The Web Is Changing The Rise of Mobile Engagement Mobile Apps in Disaster Response The Rise of User-Controlled Video Information Information via Voice Systems Information on the Go Practitioner Profile: Lach Mullin, Benton County Emergency Management Location, Location, Location: The Power of Geospatial Technologies and the Environment on Social Systems Location-Based Social Networking Potential Emergency Management Uses of Location-Based Social Networking Impact of Geospatial Programming Enhanced Information via Physical Interface Practitioner Profile: Cheryl Bledsoe, Clark Regional Emergency Services Get Your Head into the Cloud: Available Tools and Systems to Improve Emergency Management Functions Open Government and Gamification Functional and Accessibility Challenges Hackers, Zombies, and Second Life: Potential Improvements to Operational Efficiency Additional Web 2.0 and Social Media Tools Collaborative and Contributory Systems Practitioner Profile: Kim Stephens, Emergency Management Researcher, Practitioner, and Blogger Appendix A: Disasters Referenced by Chapter Appendix B: Answer Key Index <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Rumors <s> In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter. <s> BIB003
A rumor is an unverified piece of information that circulates in the situation of uncertainty by people who want to make sense of their situation  . Rumors, when verified, form two categories of true and false. False rumors can cause panic and distress especially after the onset of a major disaster. A bold example of false rumors is reported after a few cases of Ebola infection were reported in Newark, Miami Beach, and Washington DC. The rumors state that Ebola can spread through water, food, and air  . Also after hurricane Sandy, several false rumors have been observed. More than ten thousand tweets which contained fake images circulated after hurricane Sandy and the analysis show that top 30 users in this diffusion process were responsible for 90% of the tweets BIB003 . This observation verifies the fact that these images were injected into the stream of Sandy-related tweets by a small group of illegitimate users. There are two mechanisms for manually correcting false rumors after a disaster. The first one is the self-correcting power of the crowd. According to disaster psychologists, social media systems will eventually correct erroneous information as both the sender of the information and other members of that community seek validation of their posts BIB002 . A study by Mendoza et al. BIB001 shows that majority of tweets which are related to a true rumor are affirming and this percentage is much lower in regard to false rumors. This suggests that Twitter community works as a collaborative filter for information. However, this mechanism is effective when a false rumor is not originated intentionally and enough time is given to this process to converge. In the aftermath of disasters such as hurricane Sandy, these conditions are not satisfied and so there is a need for emergency managers and officials to intervene which is the second method of correction. An example of this mechanism is the tweet which was posted by ConEdison energy company indicating that none of its employees have been trapped in a damaged plant and end the circulation of rumors about it (this tweet can be accessed via https://goo.gl/TC1Slx). The aforementioned rumor correction methods are time- consuming and dependent on manual efforts which make them less effective as the social media becomes more prominent as the information channel after disasters. More than 300,000 tweets per minute after Japan Earthquake in 2011 and more than 20 million tweets after hurricane Sandy show the volume of data that needs to be processed to extract rumors. To overcome this challenge, methods have been proposed to automatically detect tweets in the swarm of social media posts by using their specific behaviors. For example, the diffusion process of rumors is different from those of normal posts. The originality of posts is lower (most of the posts are retweets). The number of users involved in the cascade is low in comparison to its virality. Also, the depth of the resulting cascades are lower than those of normal posts as users receive information by search and the posts, mainly, do not diffuse in the friendship network BIB003 .