2017 IEEE International Conference on Big Data (BIGDATA)
Mining Social Media for Disaster Management: Leveraging Social Media Data for Community Recovery
Yuya Shibuya The University of Tokyo The Graduate School of Interdisciplinary Information Studies Tokyo, Japan e-mail:
[email protected] Abstract—Social media data, from Twitter and Facebook, for example, can be regarded as critical information sources during disasters through their use in detecting and assessing disaster situations. This study overviews relevant literature from the perspective of social media for disaster management. The findings of this study show that while many previous studies have focused on how to leverage social media data for mitigating and responding to disasters, few have focused on social media use for a disaster-struck community’s recovery. This paper also argues that there is a need to study the correlations between social media data and the affected people’s recovery activities in the real world. With this gap in mind, the author discusses one potential avenue for future work. Keywords-component; Social Media; Disaster management; Twitter; Recovery; Resilience
I.
INTRODUCTION
We have seen a proliferation of social media use, such as with Twitter and Facebook, during man-made and natural disasters over the last decade. The way such media has changed how people communicate and gather information in the aftermath of disasters is dramatic. Scholars have analyzed how we can leverage social media data to detect and assess disaster situations, but most researchers have focused on social media data right before and right after disasters. To the author’s best knowledge, however, there is a paucity of research into how social media could be used to analyze and gauge community resilience to disasters. Although detecting and assessing situations during disasters are essential tasks, this paper argues that there is enormous potential to utilize social media data to look into affected people’s activities so that they can regain their normal lives. More precisely, this paper discusses the need to investigate how to utilize social media data for the recovery phase and how to analyze community’s recovery status by looking into the correlation between people’s conversations on social media and the affected people’s activities in the real world. The rest of the paper is organized as follows. In Section II, this paper provides a background to the current study from the perspectives of disaster management history and citizen participation. In Section III provides a review of related works by focusing on social media mining in disasters. In Section IV, based on the findings of the literature review, a future research framework is proposed. Lastly, in Section V, the conclusion is given.
978-1-5386-2715-0/17/$31.00 ©2017 IEEE
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II.
DISASTER MANAGEMENT AND CITIZEN COPRODUCTION
A. Brief History of Disaster Management and Citizen Participation Disaster management research initially expanded during the Cold War, when planning for nuclear war and the building of bomb shelters was encouraged, and then once the threat of nuclear war ebbed, research focus gradually moved to how to respond to natural disasters [1]. Although disaster management originated with a para-military and top-down approach, it began to step toward a community-based and collaboration-based approach, where various types of actors including citizens were involved [2]. Around the 1980’s, governments started to make recovery plans based on citizen opinions, and citizen engagement in recovery efforts started to get more attention with the goal of helping the affected community recover more efficiently and effectively [3]. Furthermore, ICT (Information and Communication Technology) use during disasters has led to more citizen engagement in disaster responses, especially since smartphones and social media became popular. During disasters citizens started to create communities in cyberspace. Social media presented new ways to explore decentralized communication during disasters and how social life could be enacted in new or hybrid ways [4]. Social media has evolved to become interactive, collaborative, conversational and community-based for crisis communication [5]. According to [6], the Indian Ocean tsunami of 2004 and Hurricane Katarina in 2005 revealed the coming age of the online disaster response community. For example, people created interactive information exchanges in seeking family members and in identifying shelters. This new phenomenon of using ‘people as a sensor’ [6] or ‘citizen sensing’ [7], [8] , helped build rapid response databases. Social media facilitated knowledge sharing by increasing knowledge reuse and by eliminating the reliance on formal liaison structures used previously to share knowledge between different agencies [9]. B.
Disaster Management and Social Media Obtaining and managing information regarding disasterrelated situations is a critical issue during disasters. This is because disasters always involve massive uncertainty. To this
end, a variety of valuable contents are generated on social media during disasters. Information from social media has the potential to complement traditional situational awareness techniques, such as surveys because social media can provide fine-grained measurements of behavior over time while taking advantage of significant population sample sizes [10]. Among researchers and practitioners, the importance of the inclusion of the cyber community and knowledge sourced from individuals has become essential since the Haiti earthquake of 2010 and the Great East Japan Earthquake and Tsunami of 2011 [11], [12]. Moreover, by the time of Hurricane Sandy in 2012, using social media had become an important part of disaster response due to the increasing popularity of social media such as Twitter and Facebook as well as the availability of information posted by individuals [11]. III.
OVERVIEW OF SOCIAL MEDIA MINING FOR DISASTER MANAGEMENT
In this section, relevant studies of social media use during disasters from the perspective of citizen sensing is discussed. In identifying previous studies, the author relied primarily on the online database, the Web of Science. First, related works were searched by key terms, such as disaster, hurricane, typhoon, social media, and Twitter. Among the articles identified through the key terms, the author selected only those related to the aim of this paper. Because the current study focuses mainly on citizen sensing, the author did not concentrate on the role of mass media and the interaction between traditional media and social media. In addition, in this paper, the author explores literature which examines both man-made and natural disasters because, although the two types of disasters have their own features, many studies treat both man-made disasters (e.g., the Boston marathon bombing) and natural disasters (e.g., Hurricane Sandy in 2012) as cases in a single study at the same time. In total, 70 studies were selected. Two perspectives for choosing relevant literature were selected by the author; namely, 1) approaches that harness social media data for disaster management and 2) approaches that target the phases of a disaster. Although there are several ways to distinguish these phases, the related works were chosen to represent three phases: mitigation, response, and recovery1. A.
Mining Social Media for Disaster Management Many studies focus on mining social media for the response phase [13]–[22] and the mitigation phase [20], [23]– [25]. For example, with the aim of improving the response phase, research has been conducted regarding how information is spread over social media during disasters [26]– [30]; how affected people use social media during disasters [31], [32], and how social media contents changes as time passes [33], [34]. For the mitigation phase, detecting unusual
1 The response phase in this paper refers to right after an event until several weeks to several months later. The mitigation phase in this paper refers to pre-disaster periods to right before disasters.
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events based on a tweet’s features such as its keywords and its context has been well studied [20], [23]–[25]. A difficult problem in mining data during unexpected events is the high volume and velocity of the data stream. To catch the most meaningful contents in time critical situations, multiple filtering approaches have been developed [22], [28], [29], [35]–[46]. For example, [47] proposes an algorithm that classifies disaster-related tweets using hashtags and named entities. Most of these studies analyze social media data by applying machine learning techniques and natural linguistic programming (NLP). In addition, several studies have applied network analysis [48]–[51]. For example, [52] characterizes social media communities during disasters by identifying the top central users. Visualization of disaster-related data is also addressed by several studies because it is important to understand time critical situations instinctively [14], [15], [18]. B.
Sentiment Analysis Sentiment analysis is a multidisciplinary field of study that analyzes people’s sentiments, attitudes, emotions and opinions about different entities such as products, services, individuals, companies, organizations, events and topics [11]. In the context of disasters, public mood and emotion after disasters have been analyzed broadly [50], [53]–[56] For instance, reference [57] analyzes tweets during Hurricane Sandy and describes how people’s sentiments changed based on their distances from the disaster. C.
Connection between Conversations on Social Media and Physical Actions in the Real World The studies summarized above provide new channels into disaster management, but fewer studies focus on the relationships between social media contents and activities in the real world. In other words, most studies focus only on what is going on in the cyber world, such as communication trends in social media, yet they do not look into what the findings from the cyber world mean in the real world. For example, a study analyzing whether social media trends can be an indicator of real world data, such as a disaster-struck community’s status or the actual amount of damage, is needed. This is because linking cyber and physical worlds’ findings is critical to improve the sensing in disaster-related situations. On the other hand, in other fields, such as public health and marketing studies, investigating the relations between cyber and the physical spaces in activities is popular. For example, in the public health field, the correlations between a pandemic and Twitter data has been studied with the aim of detecting epidemic outbreaks as soon as possible [58]–[60]. In the market and business fields, correlations between twitter sentiment and stock price [61] and the value of the Dow Jones Industrial Average over time [62] have been studied. Similarly, [63] finds the correlations between the value of online movie ratings and motion picture revenues. The recovery phase in this study refers to a disaster-struck community’s reconstruction periods which usually takes several months to several years.
In the context of disaster management, analyzing the relationships between the cyber and physical world is still its infancy compared to other fields. Some examples addressing the correlation between the cyber and the physical world are investigations of the relationships between chatter on Twitter and donation amounts [64], the proximity of a hurricane path and social media activities [65], and sentiment on social media and actual disaster damage [66]–[68]. Several studies also argue that social media can be used as earthquake detectors [23]–[25], water level sensors or detection of floods [69], [70], and sensors of people’s evacuation activities [16], [71]–[73] by comparing social media data with real world data. These studies suggest that significant data on social media can promote “citizen as sensor” approaches for disaster management, providing effective and near real-time complement data for detecting or assessing disaster-related incidents [72]. TABLE I. Paper [10] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]
Target/Sample Data
Phase
b
Approach
A B C
1
2
Hurricane Sandy in 2012 x Japan Earthquake and Tsunami x 2014 flood in Malaysia x Hurricane Sandy, 2013 Boston x Marathon Bombing None x Typhoons, Earthquakes, Winter x storms, Thunderstorms, Wildfires Kumamoto earthquake, Hurricane x Sandy Hurricane Katrina, London bombings, 2007 shooting at Virginia Polytechnic x University None x None x 2012 UK floods x x 2015 Rainbow typhoon x Hurricane Sandy x Hurricane Sandy x Hurricane Sandy x Hurricane Sandy x Osama Bin Laden’s capture and death, Hurricane Irene, Hurricane x Sandy, The US 2012 Presidential Election Hurricane Sandy x Hurricane Sandy x Japan Earthquake and Tsunami x x Hurricane Sandy x Alamo Gas Leak, Oklahoma Tornado, Hurricane Sandy, South Dakota x Blizzard Paris Shooting, Hurricane Arthur, x Boston Bombing Hurricane Sandy x 2015 Nepal Earthquake x 2013 Alberta flood x 2010 Haitian earthquake, Japan earthquake and tsunami, x 2011 Hurricane Irene Mexican drug war, x Egyptian revolution,
x x x
x
x
x
x
x
[42] [43] [44] [45] [47] [46] [48] [49] [51] [52] [50] [23] [24] [25] [53]
RELATED WORK REGARDING SOCIAL MEDIA AND DISASTER MANAGEMENT a
Paper
[54]
3
[55] [56] [57] [74]
x [59] [60] [75] [64] [65] [71] [72] [73] [69] [70]
x x x x x x x x x x
x
[66] [67] [68] [76]
x
[77] x x x x
[78] x
[79] [80] [81] [82] [83] [84]
x x
Target/Sample Data Indonesia Volcano Eruption and so forth Hurricane Sandy None Nepal earthquake Nepal earthquake Hurricane Sandy 2013 Queensland Floods Hurricane Sandy 2011 Libya crisis, Japanese Earthquake and tsunami Typhoon Haiyan Badkid, Boston Marathon Bombing, Brazil World Cup Riot, NBA Finals 2014, Sandy Hook Elementary Shooting 2011 England Riot, Hurricane Irene, 2011 Virginia Earthquake Hurricane Sandy Japan Earthquake and Tsunami Japanese earthquakes 2014 South Napa earthquake Sewol ferry disaster Japan Earthquake and Tsunami, Haiti earthquake, H1N1 outbreak Japan Earthquake and Tsunami 2013 Oklahoma tornado Hurricane Sandy 2014 Isla Vista killings, Northern Arizona University Shooting, 2015 Umpqua Community College shooting Haitian cholera outbreak 2009 H1N1 Boston Marathon Bombing Hurricane Sandy Hurricane Sandy Hurricane Sandy Hurricane Matthew Forest Fires/Haze 2013 Floods in China and India 2013 River Elbe Flood Hurricane Sandy, 2008 England Earthquake Hurricane Sandy Hurricane Sandy 2013 Earthquake and Typhoon in the Philippines None Yushu earthquake, Beijing rainstorm, Yuyao flooding the Nepal Earthquake, the Gurudaspur Terrorist attack Typhoon Haiyan None Japan Earthquake and Tsunami Hurricane Patricia 2013/14 UK Storms and Floods
Phasea Approachb A B C
1
x x x x
x x x x
x
x
x x x
x
x
x
x x
x
x
x
2
3
x
x
x
x
x
x x x x x x x x
x x x x x
x x x
x
x
x
x x x x
x x
x x x
x
x
x x x x x x x x x x x x x x x
x x x x x x x x x x
x
x x x x x x x x
x
x
x x
x x
x x
x
x
x x
x
x
x
x
x
x
x
x
x x x
x
x x x x x x
x x
x
a. A represents mitigation phase (e.g., detecting earthquakes. B represents response phase (e.g., assessing the situation of damaged areas, and people’s needs and moods). C represents recovery phase (e.g., analyzing long-term recovery process and progress). b. 1 represents collecting disaster-related data and contents analysis. 2 represents analyzing sentiment. 3 represents comparing cyber and physical data.
x x x x x
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TABLE II.
SUMMARY OF TARGET PHASES AND STUDY APPROACHES OF RELATED STUDIES
1.Content Analysis
Phase
16 61 5
A. Mitigation B. Response C. Recovery
Approach 2.Sentiment Analysis 2 21 4
3.Comparing cyber and physical worlds data 4 10 1
A Number in a cell represents the number of related papers. One work can be categorized into several cells.
IV.
DISCUSSION
In this section, the author briefly summarizes the limits of the previous studies, then discusses one possible avenue for future study, which addresses harnessing social media data for the recovery phase and investigating the relationships between the cyber and physical world. Table I presents an overview of previous work regarding social media data and disaster management, showing their target disaster phases and approaches. Table II shows the numbers of each target disaster phase and study approaches. As shown in Table I and Table II, and as described in the previous section, social media usage during disasters is being studied more and more these days. However, there are some shortfalls. This paper finds two main areas that have been not thoroughly studied yet. First, there is a need to study social media use not only for disaster mitigation and response phases, but also for the recovery phase. As shown in Table II, despite a variety of studies addressing how to leverage social media data during disasters, they mostly focus on mitigation and response phases. The research community has rarely explored social media data for the recovery phase [76]. Second, bridging physical and virtual spaces by comparing data from the two is needed. To promote accuracy and a high quality of monitoring and assessing disaster, studying the relationships between the physical and virtual world in the context of disasters is needed. •
Future Study Framework: Assessing Community Resilience As described in section III , some of the related studies have contributed to connecting social media data with realtime sensings in the context of disaster, such as detecting unusual evetns and assessing damage. However, there are less studies that focus on mid-to-long term activities; in other words, a disaster-impacted community’s recovery activities. More specifically, a socio-economic perspective is needed because traditional socio-economic recovery indicators, such as observing changes in population, consumption, Gross Domestic Production (GDP) or Gross Prefectural Production, are only available yearly or monthly and there is a considerable time lag before these conventional socioeconomic recovery indicators get published (e.g., this month’s data will be published several months later). Therefore, finding recovery proxy on social media, which can be
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observed in a real-time way, can complement the traditional way to monitor and assess community recovery. Furthermore, these days, in disaster management, the concept of ‘resilience’ as the citizens’ ability to respond and recover from disruptions has gained wide use [3], [85]. One of the ways to measure resilience is through a reduced time for recovery (restoration of a specific system or set of systems to their ‘normal’ level of performance) [86]. To reduce the time to recover from disasters, there is a need to assess and monitor community recovery so that recovery policies and outside help can be managed and appropriately provided. Although several studies have tried to assess community recovery after disasters by bridging cyber and physical world they investigate social media data in the domains of trauma study and tourist recovery [76], [83], [84]. Therefore, there is a need to investigate socio-economic recovery by applying multiple analysis methods and combining social media data with people’s recovery activities in the real world. Motivated by a need to fill the gaps between related works, the author proposes a conceptual framework to monitor and assess a community's recovery status, mainly focusing on socio-economic recovery by connecting virtual and physical activity data (Figure 1.). As shown on the left side of Figure 1., social media data can be leveraged as ‘citizen as sensor’ by applying multiple methods including, analyzing social media topics, capturing human mobility after disasters based on geotagged social media data, and analyzing an affected community’s mood by sentiment analysis. Then, the relationships between these cyber world data and physical world data, which described in the right side of the framework, activity in the real world, should be analyzed. In the context of disaster management, activities in the real world can be sensed by roughly two types: real-time sensing and mid-tolong term sensing. Real-time sensing is effective during mitigation and response phases, where time pressure is high and detecting incidents and assessing damage as soon as possible can save more lives and reduce damage. On the other hand, mid to long-term sensing is needed for the recovery phases, where continuous efforts to rebuild a damaged community is important. Furthermore, real-time sensing can be described in one direction, pictured in the middle-upper part of Figure 1.; monitoring observable indicators on social media, and once an incident is assessed (such as ‘we need help’ or ‘we need water’), one can respond to and deal with that incident or situation (such as sending rescue teams or transporting water). On the other hand, because recovery phases usually take more than several months or even several years, mid to long-term sensing should not be done in one direction, but in a circulated way, which is similar to the PDCA circle (Plan, Do, Check and Act) as described in the middle lower part of the Figure 1. For example, once an impacted community’s recovery status is assessed, recovery plans can be made and implemented. Then after assessing the effect of the recovery plan, a policy can be amended and a new plan for assistance made if needed. For mid-to-long sensing, it is important to realize that some indicators can be latent. For example, in assessing the level of a disaster-affected community’s consumption recovery, not only direct words (e.g., ‘comsuption’ and name of goods)
Real-time sensing Citizen as Sensor
Monitor/Assess observable indicator
(1) Contents Analysis What kinds of topics are on social media
Respond to and deal with incidents Detect incident / Assess situation
Observable Indicators Social Media Data
(2) Geotagged data Analysis People mobility in affected areas
Analyzing correlations
(3) Sentiment Analysis Mood and sentiment among people
Latent Indicators
・ ・ ・
Monitor/Asse ss
Cyber world
Latent indicators
Plan
Activity in the real world
Real-time sensing e.g., Detecting incident such as an earthquake and pandemic Damage assessment
mid-to-long term sensing Socio-economic activity e.g., buying common goods that are needed for reurning normal circumstances
e.,g., Make
Recovery Recovery policy
mid-long term sensing
Proxy of Recovery?
Physical world
Intervention e.g, Implement policy
Figure 1. Conceptual study framework for analyzing social media data for recovery phases indicate consumption recoveory, but also other latent words or topics, such as people’s mobility or sentiment, which might have correlations with the consumption recovery. V.
CONCLUSION
This paper explored studies concerning social media mining for disaster management. Considering these previous studies, this paper has raised two issues that have not been adequately addressed by the predominant literature. First, there is a need to research not only mitigation and response phases, but also there is a need to research the way to assess and monitor post-disaster recovery phases. Secondly, although multiple approaches have been applied to social media analysis for disaster management in the last decade, such as geo-tagged data analysis, sentiment analysis, network analysis, and contents analysis as well as filtering eventrelevant data, this paper argues that, there is a need to investigate correlations between social media data and socioeconomic human activities in the real world. More precisely, the author argues that we need to analyze the relationships between traditional socio-economic recovery indicators and social media communications to find recovery indicators from social media data that can let us know the big picture of disaster-affected community’s recovery status in a real-time way. Motivated by assessing and monitoring community recovery status after disasters, this paper proposes a conceptual framework for future study (Figure 1.). Making the recovery phase shorter so that an affected community can return to their normal circumstances is an important aspect of
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community resilience. Although mining social media for monitoring and assessing community recovery has not been studied extensively in the literature, the author believes that by analyzing a community's recovery status, recovery policies and ways of providing assistance for affected communities effectively and efficiently can be improved. REFERENCES [1]
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