Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature

Abstract

Disasters are sudden and catastrophic events with fatal consequences. Time-sensitive information collection from disaster zones is crucial for improved and data-driven disaster response. However, information collection from disaster zones in a prompt way is not easy or even possible. Human-centric information provided by citizen sensors through social media platforms create an opportunity for prompt information collection from disaster zones. There is, nevertheless, limited scholarly work that provides a comprehensive review on the potential of social media analytics for disaster response. This study utilizes a systematic literature review with PRISMA protocol to investigate the potential of social media analytics for enhanced disaster response. The findings of the systematic review of the literature pieces (n = 102) disclosed that (a) social media analytics in the disaster management research domain is an emerging field of research and practice; (b) the central focus on the research domain is on the utilization of social media data for disaster response to natural hazards, but the social media data-driven disaster response to human-made disasters is an increasing research focus; (c) human-centric information intelligence provided by social media analytics in disaster response mainly concentrates on collective intelligence, location awareness, and situation awareness, and (d) there is limited scholarly research investigating near-real-time transport network management aftermath disasters. The findings inform authorities’ decision-making processes as near-real time disaster response management depending on social media analytics is a critical element of securing sustainable cities and communities.

1. Introduction

Catastrophic events occur following either natural disasters or man-made disasters in a disaster zone. Disasters cause not only economic losses to societies but also loss of lives in many cases. Over the past several years, the frequency of destructive disasters has dramatically increased, causing a huge amount of damage all around the globe [1]. This increasing frequency of disasters brings great challenges to the humankind [2], and these challenges, unfortunately, are expected to continue due to the changing global environment that triggers the disasters [3].

Disaster response, which refers to all actions taken by all parties during the aftermath of a disaster to alleviate the detrimental effects of the disaster [3], requires systematic efforts to analyse the consequences of the actions. Therefore, informed decision-making depending on data captured from a disaster zone to take corresponding actions is a must for disaster response. In other words, disaster response is a time-sensitive process that requires an immediate collection of the data from dispersed data resources in the disaster zone. However, often such an immediate collection of the data is not easy or even possible [4].

In parallel with the increase in the popularity of big data analytics, which can be defined as the methodologic approaches to capture and analyse big datasets using cutting-edge algorithms, crowdsourcing has drawn the attention of many researchers in different research fields under the name of big data analytics [5]. Since recent technological advancements enable the researchers and emergency management practitioners to capture human-centric information under various conditions including disasters, crowdsourced data gathered in a disaster zone through the participation of a large group of individuals in the disaster zone can be utilized for disaster response [3,4].

Social media platforms, especially over the past decade, have become the main mass communication channels gaining tremendous popularity on the global scale [2,3,4]. Consequently, social media data, as a sort of crowdsourced data, are ever-growing thanks to the increasing technical competence of the current social media platforms [6]. Therefore, social media analytics have become a natural research interest in many research domains, e.g., business, tourism, and hospitality, and disaster management is not an exception [7]. Social media analytics can be defined as the methodological approach to gather and find the meaning in the crowdsourced data provided by social media platforms where people have become the natural data providers.

Even though social media has a big potential to serve as a crowdsourcing tool for disaster response [8], there is no previous scholarly work that provides a systematic and comprehensive literature review to understand how social media has been utilized for improved and data-driven disaster response. The research aims to fill this research gap by identifying the current state of the literature on the utilization of social media analytics for the disaster response. To achieve the research aim, the study focuses on two key research questions: (a) How has social media evolved from being a multi-way communication tool to a crowdsourcing tool for disaster response? (b) How can the social media data, as the form of crowdsourced data, help the authorities and community with the informed decision-making to tackle the challenges in disaster response?

2. Literature Background

2.1. Challenges in Disaster Response

Disasters are sudden and catastrophic events causing a number of severe disruptions to society, and everyone across the globe has a risk to be exposed to a range of disasters. Disaster management is a process that starts with preparing the societies for a disaster before the disaster occurs, and it continues after the occurrence of the disaster to alleviate the detrimental effects of the disaster. Disaster management is an event cycle chain that consists of the following four phases, namely preparation, mitigation, response, and recovery [9]. Disaster response refers to the actions taken by all parties during the aftermath of a disaster to reduce the impacts of the disaster on humankind [3,9]. Although each phase is a crucial element in the disaster management cycle, this study focuses on disaster response because of its highly intensive and time-sensitive nature.

Disasters, especially natural hazards, usually bring not only a catastrophic event but also cascading effects following the main catastrophic event [10]. Therefore, disaster response is an intensive and complex process that needs to alleviate the adverse impacts of multiple cascading effects occurring in a specific location within a very limited time. Since prompt actions must be taken in disaster response, the information on the cascading effects must be gathered immediately for the informed decision-making, which is the first step of disaster response [10,11]. However, the information on the cascading effects of a disaster that occurred in each spatial boundary within a very short period is hard to gather, which is perceived as the main challenge in disaster response [12,13].

The difficulties in information sharing to unfold the cascading effects of a disaster directly affect the efficiency of disaster response. Disaster response starts with the informed decision-making that requires gathering the information from a disaster zone; and the first part of gathering this information is disseminating it from the disaster affected area. Therefore, this information dissemination must be at the maximum speed [12]. However, disasters are damaging and destructive to conventional information dissemination tools including emergency speed dial numbers and centralized emergency call facilities [11,13].

In addition to the absence of conventional communication tools in many cases, disasters usually affect a group of dispersed individuals corresponding to different locations, which hinders gathering the information from the multiple locations at different times to spread the information to the emergency services, authorities, and volunteers for disaster response. Consequently, there is an urgent need for a new technology that can gather the information about cascading effects happening in different locations and the information on victims that are in multiple locations, replacing the conventional communication tools. Therefore, the challenges in spreading this information promptly to the multiple parties can be achieved.

Disaster response is a collaborative effort in which multiple parties carry out individual different tasks within spatial boundaries and certain time periods [12]. Therefore, outsourcing the individual tasks of the parties in disaster response is location-dependent, and this outsourcing is always done under strict time constraints [12]. The assignment of these diverse individual tasks in specific locations within a very limited timeframe requires constant crisis communication. Although disaster response follows previously prepared solid crisis communication plans in theory, there are challenges and obstacles that disturb the crisis communication in a disaster zone because of the highly complex nature of cascading effects of the disaster [13]. To achieve the challenge of organizing constant and prompt crisis communication in disaster response, collective intelligence is required to provide collaboration and group efforts for disaster response in the disaster zones.

The geographic information of the cascading effects, following the main catastrophic event, and the victims are necessary information for disaster response. In many cases, disaster response teams need to be on site in different geographic locations at different times. The evacuation of the victims, saving their lives by taking timely actions, and the distribution of basic needs including food, water, and medication in disaster zones are the components of disaster response that require promptly gathered location information from the disaster zones. However, the wider the disaster zone is, the harder it is to gather the location information from the multiple sources in the disaster zone. Consequently, the disaster response teams need a data pipeline that can provide the teams with a constant geographic location awareness of cascading effects, the victims, and their needs so that the disaster response teams can reach the locations with the possible minimum disaster response time.

Disaster zones are severely damaged and risky areas, in which damage and risk assessment need to be conducted immediately [3]. A prompt damage assessment provides authorities with the information about the severity of a disaster; hence, the extent of the cascading effects of the disaster can be understood in a better and quicker way. Furthermore, this understanding enables the disaster response teams in the disaster zone to evaluate the risks surrounding them during the disaster response. As a result, the safety of the evacuation from the disaster zone and the continuity of the actions to save the lives and to distribute the basic needs in the disaster zone can be secured.

In addition to the disaster response teams, the victims in the disaster zone need to know the possible risk items in the disaster zone so that they can protect themselves in a better way and to assist the disaster response teams in the disaster zone. However, knowing the risk items in highly risky areas requires the authorities to conduct near-real-time assessment of the constantly changing situations in the disaster zone. This constant assessment of the changing situations in the disaster zone can only be possible by constant transfer of the near-real-time information on what has happened and what might happen from the disaster zone to outside of the disaster zone [1,4].

2.2. The Role of Social Media Analytics in Disaster Response

The term crowdsourcing was introduced by [14], and the academic interest in crowdsourcing tools for disaster response started with Ushahidi which is a crowd-mapping application utilized for disaster response after the Haiti earthquake happened in 2010 [15]. Humankind, in the aftermath of such a disaster, has become a natural information seeker, relying on any publicly available information source [16,17] and any publicly accessible communication tool [18]. An increasing number of people who use social media platforms, which are publicly accessible platforms, to share information on their daily lives make social media platforms natural crowdsourcing tools [16]. While many crowdsourcing tools for disaster response have been explored in the last decade [19,20], social media platforms as crowdsourcing tools, even if they have been proven as reliable information dissemination tools in the aftermath of disasters, have not been systematically explored [21,22,23,24,25,26].

The study conducted by [27] reveals that the human-centric information can be published in 13 different content categories using social media platforms, which proves the potential of social media to replace the conventional communication tools for not only crisis communication purposes but also gathering the information from multiple sources in different locations at different times and storing the information in a centralized way. Refs. [28,29] advocate that capturing human-centric information from a disaster zone by utilizing social media analytics to address the challenges in disaster response has become an integral part of disaster response.

Furthermore, the increasing capability of deep learning and machine learning algorithms that can assess the text and visual forms of information makes the human-centric information gathered by social media platforms more useful for improved and data-driven disaster response [30]. Although earlier studies have considered social media as only a multi-directional communication tool for disaster response [31], through social media platforms, (a) individuals can participate in collective intelligence for a collaborative disaster response aiming at providing decision-makers with a range of information to consider for reducing losses associated with the disasters [32]; (b) individuals can improve location awareness for disaster response by sharing volunteer geographic information from the disaster zones [33]; and (c) individuals can improve situational awareness for disaster response by providing on-site near-real-time information on the changing situations in the disaster zones [30].

Numerous regions worldwide, especially in the last decade, have been stricken by natural hazards including earthquakes, flooding, hurricanes, and typhoons. In addition to these natural hazards, man-made disasters cause catastrophic consequences [34], and [35] (p. 36) indicates the fact that “the world is faced with a mixture of old, new and in-between types of disasters and crises” in an average day. Similarly, Ref. [34] divide the disaster themes into four groups, namely accidents, natural hazards, public health events, and social security events. All these disasters require an improved and data-driven disaster response to address the challenges of collective intelligence, location awareness, and situation awareness in the disaster response. Therefore, this systematic literature review focuses on the utilization of social media analytics for the disaster response to not only natural hazards but also human-made disasters and accidents (large-scaled), public health events, and social security events.

3. Materials and Methods

To understand the potential of utilizing social media analytics for the disaster response, a systematic literature review was undertaken to investigate (a) how the social media has evolved from being a multi-way communication tool to a crowdsourcing tool for disaster response and (b) how the social media analytics help the authorities and the community with informed decision-making to address the challenges in the disaster response. The systematic literature review mainly followed three stages, namely (a) planning, (b) conducting, and (c) reporting proposed by [36], and the conducting stage of the methodology further adopted the approach proposed by [37] following the PRISMA diagram as shown in Figure 1.

Firstly, in the planning stage, Google Scholar was used to have a general understanding of the challenges in disaster response and the potential of social media platforms as a crowdsourcing tool to overcome the challenges. Then, the planning stage involved developing the research aim, the research questions, a list of keywords, and the creation of the search string to conduct the literature selection. The research aim and the research question were framed to generate insight into understanding the potential of social media to overcome the challenges in disaster response relating to collective intelligence, location awareness, and situation awareness.

Then, a list of keyword sets was created to conduct a thematic search. While the study aimed at filling the research gap for disaster response in the disaster management cycle, the keyword of ‘disaster risk reduction’ was also added to the search string to extend the screening process to the studies that are related to any systematic effort to reduce the risks in a disaster zone after the disaster, which is also an integral part of the disaster response [8,12]. The keyword ‘emergency response’ was also added to the search string to extend the screening process over the studies that consider any systematic effort in emergency situations that occurred after a disaster in addition to the direct refences to the outcomes of disaster response actions taken after the disaster. The keyword “social media” was selected to exclude other crowdsourcing tools in order to create a better understanding of the role of social media in disaster response.

Secondly, the search string was organized on Boolean search line as follows: Title or Publication Title Contains the term/s: ((“disaster risk reduction” OR “disaster response” OR “emergency response”) AND (“social media”)) <OR> Abstract/Summary Contains the term/s: ((“disaster risk reduction” OR “disaster response” OR “emergency response”) AND (“social media”)) in order to conduct the initial thematic search. The initial thematic search was started with the identification of the references using the search string on a university’s online library engine that covers 396 databases, including Directory of Open Access Journals, Science Direct, Scopus, Web of Science, and Wiley Online Library, which were used to complete the thematic search.

In view of the significant advancement in digital technology in the last decade, January 2012 was selected as the milestone for the identification process. Initially, a total of 867 references were detected. Then, only peer-reviewed journal articles, the articles written in the English language, and the articles that were available online were selected, excluding edited or authored books, conference proceedings, journal editorials, articles written in languages other than English, government and industry reports, and non-academic research. After this filtration, the number of the records was reduced to 421 references. Then, these references were imported to a reference manager software (EndNote X9) to double-check the duplicated records, and there was no duplicated record detected.

These articles were then “eye-balled” to ensure that the records were consistent with the thematic search, and the titles and abstracts were assessed against the research aim. This resulted in 183 articles left after the screening. The full texts of these articles were then read to determine the relevance of the selected articles to the research aim. After the first round of full-text screening, the results were reduced to 122 articles. Finally, these records were checked to see whether the type of the data captured by social media platforms, i.e., image, textual, and/or video, was clearly indicated in the study. After another round of full text screening, the total number of the records were reduced to 102 to categorize and analyse. At this point, it should be noted that only the articles that limited their data sources to sole social media data, excluding web-harvesting data, were included into the study. At this step, articles utilizing datasets such as CrisisMMD that contain only social media data in regard to disaster and emergencies were included [38]. If the data type in the referred study was not clearly explained, the record was excluded.

The articles were categorized according to the challenges to overcome in disaster response utilizing social media data in the studies by identifying the research aims, the research methodologies, and the research outcomes.

For each article, first, the aim of the article was identified, then each analysis conducted in the study was listed, separately; after that, the outcome of each analysis conducted was identified. The decision was made by investigating which challenges are separately targeted and which challenges are simultaneously targeted in the study. As the final step, categorization was made based on the combinations if there was a combination of separate challenges targeted in the same study. In this context, the research outcome was the parameter to classify the article under each challenge. The criteria formation of the final results is as follows:

• Determine the literature relevant to the research scope by using the eye-balling technique;
• Identify the suitable articles that focus on the disaster response phase after reading the full texts;
• Narrow down the selected articles according to their data source;
• Review the selected literature again and group the articles according to the purpose of the utilization of social media data in the study;
• Confirm and finalize the challenges depending on the outcome of the study and update the shortlisted groups, if necessary;
• Finalize the categorization process for formulating the final results.

4. Results

4.1. General Observations

The selected articles, firstly, were analysed with regard to the research type, social media data platforms used in the study as the crowdsourcing tools, and the type of data captured by social media platforms in their methodologies.

The research type refers to the method used in the study to test the methodology adopted in the study. At this point, it should be noted that analytic and experimental studies shown in

Table 1. Summary of the selected articles.

Research Type	Number of Articles
Analytic	6
Analytic and Experimental	5
Case Study	91
Data Source 1	Number of articles
Facebook	11
Sina Weibo	22
Twitter	82
Others	11
Data Type 2	Number of articles
Image	16
Textual	91
Video	2
Numerical	1

¹ Some articles utilize several social media platforms. ² Some articles use more than one type data as the input.

utilized an analytical approach for the study aim and then tested the analytical approach using an experimental method in the same study. The experimental methods were usually virtual setups or scenarios created in the virtual laboratory environments to test the analytical approach used in the study. In other words, the five studies that made an ‘active’ effort to test their analytical approach were classified as “analytical and experimental” (n = 4.90%). In contrast to ‘active’ effort, six studies (n = 5.88%) adopted a ‘passive’ approach without testing their analytical approach using either an experimental method or a case study, and passive studies used only historic data. The dominant group in the selected literature used case studies to test their methodologies (n = 89.21%).

As shown in Table 1, Twitter was the most common used social media platform as the data source in the selected literature, and it was followed by the Sina Weibo social media platform which is called “Chinese Twitter” by [39]. It should be noted that some articles used more than one social media platform as the data source and more than one type of data as the input.

The four different data types captured using social media platforms in the selected articles, i.e., textual, image, video, and numerical, referred to data types that were used as input in the methodologies of the selected studies. Textual data type was the most common data type used by the selected articles. Images and videos are the other data types used by the selected articles, and these visual data are starting to be utilized more often in recently conducted studies thanks to the new deep learning and machine learning algorithms [40]. Numerical data type refers to the data type used in the studies that gather only the number of tweets or posts without considering the content of the data.

It would be a strict limitation on this study that to include only the articles which studied disaster response to natural disasters in the literature, excluding the articles in the literature that focused on disaster response to man-made disasters. Although social media data have been excessively utilized for disaster response to natural hazards, the second group focusing on disaster response to man-made disasters represents almost a quarter of the selected literature; hence, they cannot be neglected. In the selected literature, the number of the studies focused on disaster response to natural hazards is placed at the top of the list with 78 articles (n = 76.47%), followed by social security events with 9 articles (n = 8.82%). Public health events and accidents are studied in seven articles (n = 6.86%) and in five articles (n = 4.90%), respectively. In the selected literature, three studies focused on disaster response to multiple disaster themes in the studies. Ref. [34] focused on 101 major disasters, under the identified disaster themes, that happened in China between 2010 and 2017. Ref. [41] focused on the utilization of Twitter accounts of Red Cross and Red Crescent National Societies for disaster response to different disaster themes and [42] focused on Queensland Fire and Emergency Services’ Twitter and Facebook accounts for disaster response to different disaster themes. The rest of the 99 studies focused on disaster response to one type of disaster theme either using a case study (or multiple case studies) or analytical and experimental approach.

Figure 2 summarizes the number of articles that used the case study method to analyse different types of disaster themes. Only 2 case studies, out of 91 case studies (n = 2.20%), focused on the disaster response to accidents; 9 case studies (n = 9.90%) focused on the disaster response to public health events; another 9 case studies (n = 9.90%) focused on the disaster response to social security events; and the remaining 71 case studies (n = 78%) focused on the disaster response to different natural hazards. The exponential increase in the number of natural hazard-case studies in the last decade started after Hurricane Sandy, which happened in 2012, and another drastic increase in the number of the natural hazard-case studies happened after Hurricane Harvey occurred in 2017. The first case study within the selected timeline in this study that focused on the disaster response to a natural hazard was [43], and after two years, [44] was published focusing on the potential of social media use for the disaster response to a social security event, which clearly shows that the great potential of social media analytics for disaster response to various disaster themes other than natural hazards was explored very shortly. COVID-19 is another reason for the increased number of case studies in the public health events. The simultaneous increasing trend in the case studies cross the four disaster themes after 2020 mainly stems from the increasing popularity of deep learning models in social media analytics that improves the analysis of the unstructured data captured by the social media platforms for disaster response [45].

The 102 selected articles were published in 68 different journals in multiple research domains, reflecting the applicability of the current social media platforms in disaster response.

Table 2. Journal distribution of the selected literature.

Code	Journal	Number of Articles	2020 SJR Score and Quartile
1	International Journal of Disaster Risk Reduction	12	1.16 (Q1)
2	ISPRS International Journal of Geo-information	5	0.68 (Q1)
3	International Journal of Information Management	5	2.77 (Q1)
4	Sustainability	4	0.61 (Q1)
5	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences	4	0.26 (N/A)
6	Natural Hazards	3	0.76 (Q1)
7	International Journal of Digital Earth	3	0.81 (Q1)
8	Social Network Analysis and Mining	2	0.46 (Q1)
9	PloS One	2	0.99 (Q1)
10	Journal of Public Health Management and Practice	2	0.77 (Q2)
11	Journal of Contingencies and Crisis Management	2	1.01 (Q1)
12	International Journal of Environmental Research and Public Health	2	0.75 (Q2)
13	Information Systems Frontiers	2	1.09 (Q1)
14	Information Processing & Management	2	1.06 (Q1)
15	Computers, Environment and Urban Systems	2	1.55 (Q1)
16	Behaviour & Information Technology	2	0.64 (Q1)
17	Others	48

lists the journals with a minimum of two articles published between 2012 and 2023. The International Journal of Disaster Risk Reduction secures a place at the top of the list with 12 articles followed by the International Journal of Geo-information with 5 articles. Furthermore, 48 journals published in different databases that comprise almost 45% of the selected literature contain at least one article that used social media as the data source with purposes of disaster response in different domains.

4.2. Attributes of Social Media in Disaster Response

In this study, attributes of social media in disaster response were identified according to the challenges that were addressed in disaster response by utilizing social media data. Then, the attributes were categorized with regards to the aim and the outcome of the individual research article so as to provide a better understanding in the role of social media data used for overcoming challenges in disaster response rather than focusing on only the role of social media platforms as the information and communication tools for disaster response. Based on the identified patterns of the aim of the social media data utilization in the selected articles, it is concluded that the selected literature utilized social media data to overcome the challenges of collective intelligence, location awareness, and situation awareness in disaster response. These patterns align with the previously identified attributes of the conventional crowdsourcing tools (including open-source mapping platforms and map-mashups) for disaster response in the literature [16]. Appendix A 

Table A1. The list of reviewed articles.

Author	Title	Journal	Aim	Relevance	Research Type	Case(s)	Case Study Theme(s)	Attribute	Data Type	Data Source
Abedin and Babar [78]	Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire	Information Systems Frontiers	The study aims to distinguish the use of social media by formal institutions from non-institutional organisations during a natural disaster	The study empirically examines the use of social media platform by Emergency Response Organisations and Non-Institutional Organisations during emergency in Australia	Case Study	Bush Fire, 2014, Victoria, Australia	Natural Hazards	C	Textual	Twitter
Ahadzadeh and Malek [131]	Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms	Applied Sciences	The study aims to investigate the disaster related messages on social media to quick damage assessment	The study assesses an earthquake severity using social media data	Case study	Napa Earthquake, 2014, San Francisco	Natural Hazards	LS	Textual	Twitter
Alam et al. [108]	Processing Social Media Images by Combining Human and Machine Computing during Crises	International Journal of Human- Computer Interaction	The study aims to develop a social media image processing pipeline that combines human and machine intelligence	The study presents a novel approach-The Crowd Task Manager module- to capture and to filter of near-real-time imagery content in social media	Case Study	Typhoon Ruby, 2014; Nepal Earthquake, 2015; Hurricane Matthew,2016; Ecuador Earthquake,2016	Natural Hazards	S	Image	Twitter
Alam et al. [109]	Descriptive and Visual Summaries of Disaster Events Using Artificial Intelligence Techniques: Case Studies of Hurricanes Harvey, Irma, and Maria	Behaviour & Information Technology	The study aims to understand how different types of social media content types convey information to situation awareness	The study comprises several computational techniques to process multimodal social media data for disaster response	Case Study	Atlantic Hurricanes, -Harvey-Irma-Maria 2017,	Natural Hazards	S	Image + Textual	Twitter
Albris [72]	The Switchboard Mechanism: How Social Media Connected Citizens During The 2013 Floods in Dresden	Journal of Contingencies and Crisis Management	The study aims to examine how online networks are used to translate activity into on-the-ground emergency response by citizens	The study exemplifies the role of the social media use during the Dresden 2013 Floods	Case Study	2013 Dresden Floods	Natural Hazards	C	Textual	Facebook
Andrews et al. [130]	Creating corroborated crisis reports from social media data through formal concept analysis	Journal of intelligent information systems	The study aims to explore how crisis concepts can be generated using data from social media	The study proposed a multi-stage process to extract meaning from the social media to assist in decision support for emergency response	Case Study	Nepal Earthquake, 2015	Natural Hazards	LS	Textual	Twitter
Asif et al. [38]	Automatic Analysis Of Social Media Images to Identify Disaster Type and Infer Appropriate Emergency Response	Journal of Big Data	The study aims to propose a disaster taxonomy of emergency response and use the taxonomy with deep-learning-based image classification	The study uses the taxonomy together with object identification algorithms to create an emergency response pipeline	Analytic Study	n/a	n/a	S	Image	CrisisMMD dataset
Athanasis et al. [88]	The Emergence of Social Media for Natural Disasters Management: A Big Data Perspective	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives	The study aims to propose a state-of-art approach towards the enhancement of decision support tools for natural disaster management with information from social network	The study provides an approach lies in the integration of GIS systems modelling outputs with real-time information from Twitter	Analytic Study	n/a	n/a	L	Textual	Twitter
Bashir et al. [84]	Twitter Chirps for Syrian People: Sentiment Analysis of Tweets Related to Syria Chemical Attack	International Journal of Disaster Risk Reduction	The study aims to investigate the use of social media after a large-scale social security event	The study reveals that the majority of the social media data about news and information	Case study	Khan Shaykhun Syria Chemical Attack, 2017	Social Security Events	C	Textual	Twitter
Bateman et.al. [114]	Is User-Generated Social Media Content Useful for Informing Planning and Management of Emergency Events?—An Investigation of an Active Shooting Event in a U.S. Airport	Case Studies on Transport Policy	The study aims to determine if social media data is useful information source for situational awareness during the response phase of short-scale emergency events	It uses a case study approach to analyse the content of social media posts to evaluate their potential to aid with situational awareness	Case study	Fort Lauderdale Hollywood Airport Shooting, 2017, Florida	Social Security Events	S	Textual	Twitter
Beedasy [49]	Online Community Discourse During The Deepwater Horizon Oil Spill: An Analysis of Twitter Interactions	International Journal of Disaster Risk Reduction	The paper aims to understand the disaster response communication on the social media after a large-scale emergency accident	The study provides insight into the content and flow of the tweets and how the flow helped to the disaster response	Case Study	Deepwater Horizon Oil Spill, 2010, Gulf Of Mexico	Accidents	C	Textual	Twitter
Brandt [65]	Examining the Role of Twitter in Response and Recovery During and After Historic Flooding in South Carolina	Journal of Public Health Management and Practice	The study aims to examine the role and use of social media as a response and recovery strategy in a flooding event	The study demonstrates the use of social media for situational awareness purposes during a historic rainfall and flooding	Case Study	South Carolina Floods, 2015	Natural Hazards	C	Textual	Twitter
Callcut [137]	Finding The Signal in The Noise: Could Social Media Be Utilized for Early Hospital Notification of Multiple Casualty Events?	PloS one	The study hypothesizes that social media data can produce a unique signal to inform authorities to provide better emergency response	The study proposes an approach to utilize social media data to inform hospitalisation in order to provide better emergency response aftermath emergency events	Case Study	Super Bowl 50, 2016, California	Social Security Events	CLS	Textual	Twitter
Camama et al. [77]	Q-DAR: Quick Disaster Aid and Response Model Using Naïve Bayes and Bag-Of-Words Algorithm	IOP Conference Series- Materials Science And Engineering	The study aims to create a real-time approach in decision making for disaster management	The study presents the framework of quick disaster aid and response (QDAR) model for disaster response	Case Study	Typhoon Ulyssess, 2009, Philippines	Natural Hazards	C	Textual	Twitter
Canales et.al. [61]	Tweeting Blame in A Federalist System: Attributions for Disaster Response in Social Media Following Hurricane Sandy	Social Science Quarterly	The study aims to understand the public opinion using social media data	The study measures the public opinion about the government performance in disaster response	Case Study	Hurricane Sandy, 2012, New Jersey-New York	Natural Hazards	C	Textual	Twitter
Chae et al. [129]	Public Behaviour Response Analysis in Disaster Events Utilizing Visual Analytics of Microblog Data	Computers & Graphics	The study aims to identify how geo-located data can improve the social media use for emergency response purposes	The study examines the role of geo-located social media data in emergency response using a case study approach	Case Study	Hurricane Sandy, 2012, New Jersey-New York	Natural Hazards	LS	Textual	Twitter
Chaudhuri and Bose [45]	Exploring The Role of Deep Neural Networks For Post-Disaster Decision Support	Decision Support Systems	The study aims to address the problem of lack of insightful information of the disaster sites by utilizing image data from social media	The study demonstrates the capability of image analytics using social media as the image source to prioritize the resources for emergency response	Case Study	Earthquakes Tohoku Japan, 2011 and Bologna, Italy 2012	Natural Hazards	S	Image	Twitter
Cheng et al. [94]	Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data	ISPRS International Journal of Geo- Information	The study aims to propose a new approach to evaluating the near-real-time intensity of the affected population using social media data	The study successfully presents an Index- Normalized Affected Population Index (NAPI) to evaluate the affected population intensity for emergency response purposes.	Case study	Typhoon Haiyan, 2013, Philippines	Natural hazards	S	Textual	Twitter
Cheng et al. [71]	Evaluating Social Media Response to Urban Flood Disaster: Case Study on an East Asian City (Wuhan, China)	Sustainability	The study aims to understand the differences in social media participations	The study proves that there is a one-day lag participation between the public responses and the event in China which is longer compared to the Western countries	Case Study	Wuhan City Flood, 2016, China	Natural Hazards	C	Textual	Sina Weibo
Cheong and Babcock [57]	Attention to Misleading and Contentious Tweets in The Case Of Hurricane Harvey	Natural Hazards	The study aims to understand the reliability of the social media data during a disaster	The study investigates how misleading and contentious discussions are transmitted through social media aftermath a disaster	Case Study	Hurricane Harvey, 2017, Houston	Natural Hazards	C	Textual	Twitter
Dabner [43]	‘Breaking Ground’ in The Use of Social Media: A Case Study of A University Earthquake Response to Inform Educational Design With Facebook	The Internet and Higher Education	The study aims to investigate how University of Canterbury respond to an emergency using social media	The study exemplifies a case study in which social media used at the organisational level to respond a disaster	Case Study	Canterbury Earthquake, 2010, New Zealand	Natural Hazards	C	Textual	Facebook
Deng et al. [95]	A New Crowdsourcing Model to Assess Disaster Using Microblog Data in Typhoon Haiyan	Natural Hazards	The study aims to use the social media data as a tool for risk prediction	The study proposes a new method for damage assessment via social media data	Case Study	Typhoon Haiyan, 2013, China	Natural Hazards	S	Textual	Sina Weibo
Dominguez-Péry et al. [117]	Improving Emergency Response Operations in Maritime Accidents Using Social Media With Big Data Analytics: A Case Study of The MV Wakashimo Disaster	International Journal of Operations & Production	The study aims to explore how big data analytics cored with social media in the use of emergency response	The study proposes a conceptual early warning system to prevent big-scale accidents that cause emergency	Case Study	MV Wakashimo Maritime Disaster, 2020, Mauritius Coral Reef	Accidents	CS	Textual	Twitter
Dong et al. [120]	Social Media Information Sharing for Natural Disaster Response	Natural Hazards	The study aims to improve disaster relief efficiency using social media data	The study successfully presents social media data-driven analytics for improving disaster response efficiency	Case Study	5 Different Types of Disasters Happened in U.S. Between 2011–2018	Natural Hazards	CS	Textual	Twitter
Dou et al. [93]	Disaster Damage Assessment Based on Fine-Grained Topics in Social Media	Computers & Geosciences	The study aims to address the problem of content losses on social media during a disaster	The study presents a topic classification to enhance situational awareness during an emergency	Case Study	Typhoons Hato and Pakhar’, 2017, China	Natural Hazards	S	Textual	Saina Weibo
Eilander et al. [106]	Harvesting Social Media for Generation Of Near Real-Time Flood Maps	Procedia Engineering	The study aims to show how the flood observations can used for decision support during flood events	The study proposes a flood risk map using social media data	Case study	Jakarta Floods, 2013	Natural Hazards	S	Image + Textual	Twitter
Fan et al. [116]	Spatial Biases in Crowdsourced Data: Social Media Content Attention Concentrates on Populous Areas In Disasters	Computers, Environment and Urban Systems	The study aims to explore the related factors on information distribution on the social media during an emergency	The study contributes to a better understanding of the spatial concentration of social media attention in disasters	Case Study	Hurricane Harvey, 2017, Houston and Hurricane Florence, 2018, North Caroliana	Natural Hazards	CLS	Textual	Twitter
Fan et al. [59]	Crowd Or Hubs: Information Diffusion Patterns In Online Social Networks In Disasters	International Journal of Disaster Risk Reduction	The study aims to investigate the role different types of social media users in the disaster-related information diffusion	The study examines the role of hubs and crowd in the diffusion of disaster-related information on social media	Case Study	Hurricane Harvey, 2017, Texas	Natural Hazards	C	Textual	Twitter
Fan et al. [40]	A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters	IEEE Access	The study aims to test a hybrid machine learning pipeline to identify the locations during disasters	The study proposes a framework to automatically map the evolution of disaster events using social media data	Case study	Hurricane Harvey, 2017, Houston	Natural Hazards	L	Textual	Twitter
Fang et al. [121]	Assessing Disaster Impacts and Response Using Social Media Data In China: A Case Study Of 2016 Wuhan Rainstorm	International Journal of Disaster Risk Reduction	The study aims to examine the utility of social media for disaster response in China	The study concludes that there is a significant positive correlation between social media activities and precipitation intensity	Case Study	Wuhan City Flood, 2016, China	Natural Hazards	CS	Textual	Sina Weibo
Feng et al. [47]	Flood Severity Mapping From Volunteered Geographic Information By Interpreting Water Level From Images Containing People: A Case Study Of Hurricane Harvey	ISPRS Journal of Photogrammetry and Remote Sensing	The study aims to use the social media images to estimate flood severity	The study presents a novel approach for mapping flood severity using social media data	Case Study	Hurricane Harvey, 2017, Houston	Natural Hazards	S	Image	Twitter
Francesca et al. [53]	Tweeting After An Earthquake: User Localization and Communication Patterns During The 2012 Emilia Seismic Sequence	Annals of Geophysics	The study aims to understand the Twitter use depending on the distance from a disaster centre	The study analyses the geo- localized tweets and the patterns of social media use	Case study	Emilia Earthquakes, 2012, Italy	Natural Hazards	C	Textual	Twitter
Freitas et al. [99]	A Conceptual Framework For Developing Solutions That Organize Social Media Information For Emergency Response Teams	Behaviour & Information Technology	The study aims to create a message capture system to spread information among rescue teams when an emergency occurs	The study presents a framework a conceptual framework for the development of applications to treat messages from social media	Case Study	Mexico City Earthquake. 2017 and California Fire, 2017	Natural Hazards	S	Textual	Twitter
Fung et al. [113]	Social Media’s Initial Reaction to Information And Misinformation on Ebola, August 2014: Facts and Rumours	Public Health Reports	The study aims to analyse misinformation about Ebola on social media to help public health agencies	The study investigates how the volume and content of misinformation changed during an outbreak	Case Study	Ebola	Public Health Events	S	Textual	Sina Weibo+ Twitter
Grace [46]	Toponym Usage in Social Media in Emergencies	International Journal of Disaster Risk Reduction	The study aims to analyse toponym usage during an emergency	The study shows how social media users provide context for hyper-local place references when reporting infrastructure damage and service disruption during an emergency	Case study	F1 Tornado- 2017, Pennsylvania	Natural Hazards	L	Textual	Twitter
Gray et al. [62]	Social Media During Multi-Hazard Disasters: Lessons From The KAIKOURA EARTHQUAKE 2016	International Journal of Safety And Security Engineering	The study aims to examine the use of online content during an earthquake	The study presents an analysis of crisis communication using social media during an emergency	Case Study	Kaikoura Earthquake, 2016.	Natural Hazards	C	Textual	Twitter
Gu et al. [91]	From Twitter to Detector: Real-Time Traffic Incident Detection Using Social Media Data	Transportation Research. Part C, Emerging Technologies	The study aims to present a novel approach to detect traffic incidents	The study presents an approach that is capable of detecting traffic accidents via social media data	Analytic and Experimental Study	n/a	n/a	S	Textual	Twitter
Han and Wang [64]	Using Social Media To Mine And Analyse Public Sentiment During A Disaster: A Case Study Of The 2018 Shouguang City Flood In China	ISPRS International Journal of Geo-Information	The study aims to explore public sentiment during Shouguang City Flood via social media	The study provides insight into understanding the social media data over time, space and content	Case Study	Shouguang City Flood, 2018, China	Natural Hazards	C	Textual	Sina Weibo
Hao and Wang [103]	Leveraging Multimodal Social Media Data for Rapid Disaster Damage Assessment	International Journal of Disaster Risk Reduction	The study aims to combine different data modalities to damage assessment	The study successfully proposes a data-driven method to locate and evaluate disaster damage using social media data	Case Study	Several Flood And Wind Hazards In Houston And Miami	Natural Hazards	S	Image + Textual	Flickr + Twitter
Hasfi et al. [115]	Overlooking The Victims: Civic Engagement on Twitter During Indonesia’s 2019 Fire And Haze Disaster	International Journal of Disaster Risk Reduction	The study aims to examine the extent to which levels of civic engagement are shaped through social media	The study conducts a big data mapping using social media data	Case study	Indonesia’s Fire and Haze Disaster, 2019	Natural Hazards	CL	Textual	Twitter
Huang et al. [79]	Mining The Characteristics of COVID-19 Patients In China: Analysis of Social Media Posts	Journal of Medical Internet Research	The study aims to analyse the characteristics of suspected or confirmed COVID-19 patients	The study advocates that making full use of available social media platforms increases the efficiency in a public health emergency response	Case Study	COVID-19	Public Health Events	C	Textual	Sina Weibo
Huang and Xiao [56]	Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery	ISPRS International Journal Of Geo- Information	The study aims to classify the tweets depending on emergency management phases	The study presents a novel approach to separate the tweets regarding to emergency response phase from other tweets	Case Study	Hurricane Sandy, 2012, New Jersey-New York	Natural Hazards	C	Textual	Twitter
Huang et al. [104]	Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery With Real-Time Gauges And Tweets	IEEE Transactions on Geoscience and Remote Sensing	The study aims to assess the flood inundation probability via social media	The study proposes a flood inundation system using images from social media	Case Study	South Carolina Floods, 2015	Natural Hazards	S	Image	Twitter
Jiang et al. [127]	Emergency Response And Risk Communication Effects of Local Media During COVID-19 Pandemic in China: A Study Based on A Social Media Network	International Journal of Environmental Research and Public Health	The study aims to investigate the spatial distribution of the effectiveness of emergency response during COVID-19 via social media data	The study provides insight into the use of social media as a situational awareness tool by government authorities to provide emergency response	Case Study	COVID-19	Public Health Event	CS	Textual	WeChat
Karmegam and Mappillairaju [83]	Information Extraction Using A Mixed Method Analysis of Social Media Data: A Case Study of The Police Shooting During The Anti-Sterlite Protests at Thoothukudi, India	Information Development	The study aims to propose a mixed method social media analysis depending on human based techniques in an emergency event	The study presents a framework to understand public’s opinion via social media during an emergency	Case Study	Indian Police Shooting, 2018	Social Security Events	C	Textual	Twitter
Kent and Capello [90]	Spatial Patterns and Demographic Indicators of Effective Social Media Content During The Horsethief Canyon Fire of 2012	Cartography And Geographic Information Science	The study aims to examine the demographic characteristic that are produce useful information on social media during an emergency	The study successfully identifies the specifics of demographic characteristics to contribute the actions in emergency response	Case Study	Horsethief Canyon Fires, 2012, Wyoming	Natural Hazards	L	Image+ Textual	Flickr + Instagram + Picasa + Twitter
Kikin et al. [132]	Social Media Data Processing and Analysis by Means of Machine Learning for Rapid Detection, Assessment and Mapping The Impact of Disasters	ISPRS Archives	The study aims to determine the metrics to assess the relevance and accuracy of social media information during an emergency	The study proposes a verification procedure to assess the accuracy of the information for mapping and spatial analysis	Analytic Study	n/a	n/a	LS	Image+ Textual	Facebook+ Twitter
Kimet et al. [76]	Emergency Information Diffusion on Online Social Media During Storm Cindy in U. S	International Journal of Information Management	The study aims to investigate the social network in Twitter during Storm Cindy	The study identifies certain types of Twitter users were dominant as information sources aftermath a disaster	Case Study	Storm Cindy, 2017, Louisiana	Natural Hazards	C	Textual	Twiter
Kim and Hastak [63]	Social Network Analysis: Characteristics of Online Social Networks After A Disaster	International Journal of Information Management	The study aims to apply social network analysis to convert emergency social network data	The study provides insights to understand the critical role of social media for disaster responsepropagation	Case Study	Louisiana Flood, 2016	Natural Hazards	C	Textual	Facebook
Kirac and Milburn [128]	A General Framework for Assessing The Value Of Social Data For Disaster Response Logistics Planning	European Journal of Operational Research	The study aims to assess the value of social media data for disaster response planning	The study successfully demonstrates a framework on a mobile relief supply delivery using social media data in a case study	Case Study	Haiti Earthquake, 2010	Natural Hazards	LS	Textual	Facebook + Twitter
Korolov et al. [60]	Predicting Charitable Donations Using Social Media	Social Network Analysis and Mining	The study aims to understand the relationship between chatter on social media and observed actions after a disaster	The study analyses the relation between social media activity and real-life behaviour in case of emergency response	Case Study	Hurricane Sandy, 2012, New Jersey-New York	Natural Hazards	C	Textual	Twitter
Kothari et al. [82]	How Do Canadian Public Health Agencies Respond to The COVID-19 Emergency Using Social Media: A Protocol For a Case Study Using Content And Sentiment Analysis	BMJ Open	The study aims to examine how Canadian Authorities engage with the public using social media during an emergency	The study identifies the social media level of engagement with public by Health Agencies during COVID-19	Case Study	COVID-19	Public Health Events	C	Textual	Facebook + Twitter
Kryvasheyeu et al. [102]	Rapid Assessment of Disaster Damage Using Social Media Activity	Science Advances	The study aims to explore if social media data aid in disaster response	The study presents a hierarchical multiscale analysis of disaster related social media activity during a hurricane	Case Study	Hurricane Sandy, 2012 New Jersey-New York	Natural Hazards	S	Textual	Twitter
Lai [75]	A Study of Emergent Organizing and Technological Affordances After A Natural Disaster	Online Information Review	The study aims to provide practical knowledge and social understanding about social media use by authorities during a natural hazard	The study advances the knowledge of the pattern of social media used by organisations for emergency response purpose	Case Study	Sandy Hurricane, 2012, New Jersey-New York	Natural Hazards	C	Textual	Facebook +Twitter
Lai et al. [70]	Connecting The Dots: A Longitudinal Observation Of Relief Organizations’ Representational Networks on Social Media	Computers in Human Behaviour	The study focuses on the organisations involved in the emergency response using social media data	The study exemplifies a multidimensional network approach in revealing the organisations’ communication on social media	Case Study	Typhoon Haiyan, 2014, Philippines	Natural Hazards	C	Textual	Facebook + Twitter
Laylavi et al. [123]	Event Relatedness Assessment of Twitter Messages For Emergency Response	Information Processing & Management	The study aims to present a data filter approach	The study presents an event detection for fast happening emergencies- to provide better emergency response	Case Study	Sydney Hail Storm, 2015	Natural Hazards	CS	Textual	Twitter
Li et al. [110]	Disaster Response Aided by Tweet Classification With A Domain Adaptation Approach	Journal of Contingencies and Crisis Management	The study aims to explore if machine learning can be used to identify social media information for aiding disaster response	The study proposes a text classification framework for social media data for disaster response purposes	Case Study	Sandy Hurricane, 2012, USA QLD Floods, 2013, Oklahoma Tornado, 2013, Alberta Floods, 2013; Boston Bombings, 2013, West Texas Explosion, 2013	Natural Hazards + Social Security Events	S	Textual	Twitter
Li et al. [100]	Social Media Crowdsourcing for Rapid Damage Assessment Following A Sudden-Onset Natural Hazard Event	International Journal of Information Management	The study aims to explore if social media data can be utilized for rapid damage assessment	The study successfully presents a framework using textual social media data to situational awareness in emergency response	Case Study	Ridgecrest earthquake, 2019, California	Natural Hazards	S	Textual	Twitter
Li et al. [34]	Temporal and Spatial Evolution of Online Public Sentiment on Emergencies	Information Processing & Management	The study aims to analyse interactions between groups using social network theory	The study advocates that the social media can facilitate emergency management by cooperating with the government	Analytic and Experimental Study	n/a	n/a	CLS	Textual	Sina Weibo
Li et al. [92]	Localizing and Quantifying Infrastructure Damage Using Class Activation Mapping Approaches	Social Network Analysis and Mining	The study aims to test if social media images can be used for damage assessment	The study presents a novel multi-steps approach to damage assessment via social media data to improve disaster response	Analytic and Experimental Study	n/a	n/a	S	Image	Twitter
Li et al. [133]	Monitoring The Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal For A Predictive Model Using Big Data Analytics	JMIR Research Protocols	The study aims to develop a novel data-driven approach using social media data to analyse human movement at different spatial scales	The study exemplifies the spatial distribution of people in the COVID-19 affected areas using social media data	Case Study	COVID-19	Public Health Events	LS	Numerical number of the geotagged Twitter data	Twitter
Lian et al. [52]	Strategies For Controlling False Online Information During Natural Disasters: The Case of Typhoon Mangkhut in China	Technology in Society	The study aims to propose a simulation approach to manage the false online information on the Chinese social platforms.	The study presents a case study of public attitudes and opinions on a natural disaster using social media data	Case Study	Typhoon Mangkhut, 2018, China	Natural Hazards	C	Textual	Sina Weibo + WeChat
Liu et al. [135]	Towards Detecting Social Events by Mining Geographical Patterns with VGI Data	ISPRS International Journal of Geo- Information	The study aims to develop a comprehensive workflow for event detection by mining the geographical patterns of social media information	The study examines the role of social media data in detecting events by investigating the geographical patterns	Case Study	Umbrella Movement, 2014, Hong Kong	Social Security Events	LS	Image + Textual	Instagram + Twitter
Mao et al. [134]	Mapping Near-Real-Time Power Outages from Social Media	International Journal of Digital Earth	The study focuses on how the location information can be gathered from the social media text data	The study present two-steps framework for power outage detection via social media	Case Study	Sandy Hurricane, 2012, New Jersey-New York	Social Security Events	LS	Textual	Twitter
McClendon and Robinson [54]	Leveraging Geospatially Oriented Social Media Communications in Disaster Response	International Journal of Information Systems for Crisis Response and Management	The author aims to compare two different projects that use two different online sources for disaster response	The study exemplifies two different type of sources-crowdsourcing (Ushaidi) and social media (Twitter) in use of disaster mapping	Case Study	Haiti Earthquake, 2010	Natural Hazards	C	Textual	Twitter
Mehta et al. [42]	Trust, But Verify: Social Media Models for Disaster Management	Disasters	The paper aims to build a project that combined social media data analysis and participant observations	The study presents three different models for social media sourced information in the disaster response context	Analytic Study	n/a	n/a	C	Textual	Facebook + Twitter
Mendoza et al. [98]	Nowcasting Earthquake Damages with Twitter	EPJ Data Science	The study proves if they can assess the severity of an earthquake via social media	The study exemplifies the use of Mercalli Earthquake Scale utilizing social media data	Analytic and Experimental Study	Earthquakes in Chile, 2016–2017	Natural Hazards	S	Textual	Twitter
Ogie et al. [74]	Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management	Information Systems Frontiers	The study aims to understand the contribution of human sensors in emergency management depending on the information provided by the sensors	The study categorizes human sensors and their respective levels of reliability for disaster management	Case Study	Jakarta Floods, 2014–2016.	Natural Hazards	C	Textual	Twitter
Purohit et al. [55]	Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination	Computer Supported Cooperative Work	The study aims to create an approach to provide communication between seekers and suppliers	The study presents a framework that bridges seekers and suppliers in emergency response	Case Study	Haitian Eartquake,2010 and Japan Earthquake, 2011	Natural Hazards	C	Textual	Twitter
Ragini et al. [126]	Big Data Analytics for Disaster Response and Recovery Through Sentiment Analysis	International Journal of Information Management	The study aims to understand the sentiment of the affected people by disasters.	The study proposes a big data-driven approach for social media data through sentiment analysis	Case Study	India and Pakistan Floods, 2014	Natural Hazards	CS	Textual	Twitter
Rahmanti et al. [81]	Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During The COVID-19 Pandemic in Indonesia	Computer Methods and Programs in Biomedicine	The study aims to explore how communication on social media platforms can be adopted for risk communication during an emergency	The study provides insight into how social media can be utilized for government and public health officers for emergency response purposes	Case Study	COVID-19	Public Health Event	C	Textual	Twitter
Rajput et al. [58]	Temporal Network Analysis of Inter-Organizational Communications on Social Media During Disasters: A Study Of Hurricane Harvey In Houston	International Journal of Disaster Risk Reduction	The study aims to model and analyse communication networks on social media during an emergency	The study maps communication networks on Twitter to specify the organization’s roles in emergency management	Case Study	Hurricane Harvey, 2017, Houston,	Natural Hazards	C	Textual	Twitter
Rashid et al. [3]	DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications	Pervasive and Mobile Computing	The study aims to develop a scheme integrating social media data with vehicular sensor networks.	The study successfully develops a scheme for a road damage- aware-social media driven framework.	Analytic and Experimental Study	n/a	n/a	S	Image + Textual + Video	Twitter
Ravi Shankar et al. [86]	Crowd4ems: A Crowdsourcing Platform for Gathering and Geolocating Social Media Content in Disaster Response	ISPRS Archives	The study aims to improve the social media content geolocation through crowdsourcing techniques	The paper demonstrates a new approach to improve the geolocation of social media content	Case study	Amatrice Earthquake, 2016, Italy	Natural Hazards	L	Image	Flickr, Twitter, YouTube
Rossi et al. [136]	Early Detection and Information Extraction for Weather-Induced Floods Using Social Media Streams	International Journal of Disaster Risk Reduction	The study aims to propose an automated system that use the information from the social media to integrate set of different services for emergency response	The study shows how social media data can be used together with forecast weather data to link early warning to emergency response	Case Study	Flooding in Northern Italy, 2016	Natural Hazards	CLS	Textual	Twitter
Salsabilla and Hizbaron [66]	Understanding Community Collective Behavior Through Social Media Responses: Case of Sunda Strait Tsunami, 2018, Indonesia	E3S Web of Conferences	The study aims to evaluate the collective response of the population affected by Tsunami on social media	The study provides insight into community response to emergency response based on the affected (directly and indirectly affected) locations by a disaster	Case Study	Sundra Strait Tsunami, 2018, Indonesia.	Natural Hazards	C	Textual	Twitter
Santoni and Rufat [85]	How Fast Is Fast Enough? Twitter Usability During Emergencies	Geoforum	The study aims to provide insight into information distribution on social media aftermath an emergency	The study successfully exemplifies the usefulness of geotagged social media information in emergency response	Case Study	Paris Attacks, 2015; Brussels Attacks, 2016; Nice Attacks, 2016.	Social Security Events	C	Textual + Image	Twitter
Scott and Errett [119]	Content, Accessibility, And Dissemination of Disaster Information Via Social Media During The 2016 Louisiana Floods	Journal of Public Health Management and Practice	The study aims to assess the content, accessibility, and dissemination of social media communications made by government during an emergency response	The study provides insights into the use of social media as a situational awareness tool during an emergency	Case Study	2016 Louisiana Floods.	Natural Hazards	CS	Textual	Facebook + Twitter
Shan et al. [39]	Disaster Management 2.0: A Real-Time Disaster Damage Assessment Model Based on Mobile Social Media Data—A Case Study of Weibo (Chinese Twitter)	Safety Science	The study aims to explore if social media platforms can be utilized for urban damage monitoring	The study successfully presents a framework using social media data for real-time disaster damage assessment	Case Study	Tianjin Explosion, 2015 and Typhoon Nepartak, 2016, China	Social Security Events and Natural Hazards	S	Textual	Saina Weibo
Shreeti et al. [87]	Social Media Widget for Emergency Response	Issues in Information Systems	The study aims to explore how a social media tool, Twitter, can aid in emergency response	The study proposes a widget to gather and categorize social media data depending on the emergency location to help emergency response offices	Analytic Study	n/a	n/a	L	Textual	Twitter
Simon et al. [44]	The use of social media in the Westgate mall terror attack in Kenya	PloS one	The study aims to understand public opinion	The study evaluates the public feelings on a shooting event using social media data	Case Study	Westgate Mall Shooting, 2013, Kenya	Social Security Events	C	Textual	Twitter
Songchon et al. [107]	Quality Assessment of Crowdsourced Social Media Data for Urban Flood Management	Computers, Environment and Urban Systems	The study aims to assess the quality of crowdsourced social media data during flood events	The study presents two analytic methods to assess the quality of the publicly available Twitter data during flood events in Thailand	Case Study	Flood Events in Thailand, 2016–2018	Natural Hazards	S	Image + Textual	Twitter
Tim et al. [68]	Digitally Enabled Disaster Response: The Emergence of Social Media as Boundary Objects In A Flooding Disaster	Information Systems Journal	The research aims to provide insights into how social media could be used as boundary objects during a disaster response	The study conceptualizes the boundary spanning process of social media in assisting with disaster response	Case Study	Thailand Floods, 2011	Natural Hazards	C	Image + Textual + Video	Facebook + Twitter + YouTube
Ullah et al. [122]	Rweetminer: Automatic Identification and Categorization of Help Requests on Twitter During Disasters	Expert Systems with Applications	The study aims to identify and categorize re-tweets	The study proposes an analytic approach to classify the tweets that are looking for help	Analytic Study	n/a	n/a	CS	Textual	Twitter
Wang et al. [97]	Geospatial Assessment of Wetness Dynamics in The October 2015 SC Flood with Remote Sensing and Social Media	Southeastern Geographer	The study aims to understand how a social media platform-Twitter, can serve as a real-time source to leverage the post-event satellite assessment	The study evaluates the spatiotemporal dynamics of wetness after a flood using social media data as the real-time evaluation source	Case Study	2015 Floods, South Carolina, U.S.	Natural Hazards	S	Textual	Twitter
Wang et al. [101]	Rapid Estimation of An Earthquake Impact Area Using a Spatial Logistic Growth Model Based on Social Media Data	International Journal of Digital Earth	The study aims to estimate the impacted area by an earthquake utilizing social media data.	The study presents an approach to quickly estimate the impacted area following an earthquake combining social media data with spatial distribution	Case Study	2015 Nepal Earthquake And 2017 Jiuzhaigou Earthquake	Natural Hazards	S	Textual	Twitter+ Sina Weibo
Wang et al. [48]	Using Social Media for Emergency Response and Urban Sustainability: A Case Study Of The 2012 Beijing Rainstorm	Sustainability (Basel, Switzerland)	The study aims to present a case study to prove how emergency information is timely distributed	The study demonstrates a new way of applying spatial statistical analysis to analyse social media messages during a rainstorm	Case Study	2012, Beijing Rainstorm	Natural Hazards	S	Textual	Sina Weibo
Wu et al. [67]	Extracting Disaster Information Based on Sina Weibo in China: A Case Study of the 2019 Typhoon Lekima	International Journal of Disaster Risk Reduction	The study aims to understand if social media can be used as a reliable data source	The study concludes that disaster information gathered from social media can serve as a data source for disaster response	Case study	Super Typhoon Lekima, 2019, China	Natural Hazards	C	Textual	Sina Weibo
Wukich et al. [41]	The Formation of Transnational Knowledge Networks on Social Media	International Public Management Journal	The study aims to investigate how the global organizations’ social networks on social media	The study contributes to the understanding of how knowledge networks from globally and how social media can enable the disaster response process	Case Study	The study uses Red Cross and Red Crescent National Societies as the cases	Accidents and Natural Hazards and Public Health Events and Social Security Events	C	Textual	Twitter
Xing et al. [111]	Crowdsourced Social Media and Mobile Phone Signaling Data For Disaster Impact Assessment: A Case Study of the 8.8 Jiuzhaigou Earthquake	International Journal of Disaster Risk Reduction	The study aims to combine different sources of information to provide better disaster response	The study presents a model that combines social media data and mobile phone signalling data for disaster response	Case study	Jiuzhaigou Earthquake, 2017, China	Natural Hazards	S	Textual	Sina Weibo + WeChat
Xiong et al. [118]	Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter About The 2019 Chennai Water Crisis	International Journal of Environmental Research and Public Health	“The study aims to explore public opinion captured through a social media platform to understand if the information could help local governments with emergency response“	The study provides insight into what public think during a water crisis to help the government to understand the situation for emergency response purposes	Case Study	Chennai Water Crisis-2019	Natural Hazards	CS	Textual	Twitter
Xu et al. [80]	Knowledge Management for Extreme Public Health Events COVID-19: Based on TikTok Data	Journal of Knowledge Management	The study aims to investigate the direct influencing factors on information sharing on the social media platforms during an emergency	The study provides a case study to understand the characteristics of users affect information sharing regarding COVID-19 providing empirical data	Case Study	COVID-19	Public Health Events	C	Textual	TikTok
Xu and Ma [105]	Coarse-To-Fine Waterlogging Probability Assessment Based on Remote Sensing Image and Social Media Data	Geo-Spatial Information Science	The study aims to assess the probability of waterlogging using near-real-time data gathered from social media	The study proposes a waterlogging probability using social media in addition to the spatial data	Case Study	2016 Wuhan and 2018 Chengdu Waterlogging	Natural Hazards	S	Textual	Sina Weibo
Yabe et al. [89]	Modeling The Influence of Online Social Media Information on Post-Disaster Mobility Decisions	Sustainability	“The study aims to answer social networks in a disaster	The study proposes a data-driven modelling framework to predict post-disaster behaviour in emergency response phase using social media data	Case Study	Hurricane Sandy, 2012, New Jersey-New York	Natural Hazards	L	Textual	Twitter
Yeo et al. [51]	Unveiling Cultures in Emergency Response Communication Networks on Social Media: Following The 2016 Louisiana Floods	Quality & Quantity	The study explores the general nuance of culture in social media communication during emergency response operations	The study provides insight into the cultural indicators in the flood response communication using social media data in 2016 Louisiana Flood events	Case Study	2016 Louisiana Floods	Natural Hazards	C	Textual	Twitter
Yu et al. [112]	Extracting Typhoon Disaster Information from VGI Based on Machine Learning	Journal of Marine Science and Engineering	The study aims to design a social media data classification system for emergency response purposes	The study provides an analytical approach to assess a typhoon disaster to assist emergency response purposes	Case Study	Typhoon Anemone, 2012, China	Natural Hazards	S	Textual	Sina Weibo
Yuan et al. [50]	Social Media for Enhanced Understanding of Disaster Resilience During Hurricane Florence	International Journal of Information Management	The study aims to understand demographic differences affecting social media post during a disaster	The study exemplifies demographic dimensions on social media data information used for disaster response	Case Study	Hurricane Florence, 2018	Natural Hazards	C	Textual	Twitter
Zhang et al. [73]	Revealing Unfairness in Social Media Contributors’ Attention to Vulnerable Urban Areas During Disasters	International Journal of Disaster Risk Reduction	The study aims to examine the existence of unfairness in the content of social media posts made by Emerging Influential Contributors	The study exemplifies the information dissemination biases in online social networks using fairness assessment approaches for emergency response purposes during crises	Case Study	Hurricane Harvey, 2017, Houston, USA	Natural Hazards	C	Textual	Twitter
Yute et al. [96]	Supplementing Satellite Imagery with Social Media Data For Remote Reconnaissance: A Case Study Of The 2020 Taal Volcano Eruption	ISPRS Archives	The study aims to explore how social media data can be utilized to supplement satellite imagery in emergency response	The study advocates that the use of social media data along with satellite imagery for detecting ashfalls has a promising capability for situational awareness	Case Study	Taal Volcano, 2020 Philippines.	Natural Hazards	S	Textual	Twitter
Zhang et al. [69]	A Topic Model Based Framework for Identifying The Distribution Of Demand For Relief Supplies Using Social Media Data	International Journal of Geographical Information Science: IJGIS	The study aims to propose a framework that can identify the type and quantity of demand after a typhoon	The study presents a demand dictionary using social media data to improve the efficiency of disaster response	Case Study	Typhoon Haiyan, 2013, Philippines	Natural Hazards	C	Textual	Twitter
Zhang et al. [125]	A Cybergis-Enabled Multi-Criteria Spatial Decision Support System: A Case Study on Flood Emergency Management	International Journal of Digital Earth	The study aims to create a cyber-GIS enabled spatial decision support for emergency response	The study utilizes the social media data for decision-making process in emergency response	Case Study	Austin Floods, 2013	Natural Hazards	CS	Textual	Twitter
Zhu and Liu [124]	Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut	Sustainability	The paper proposed a framework to analyse the disparities on social media during a disaster	The paper reveals the temporal, spatial, socioeconomic patterns in thematic emphases on social media during Typhoon Mangkhut	Case Study	Typhoon Mangkhut, 2018 Philippines and South China	Natural Hazards	CS	Textual	Sina Weibo

Notes: n/a = not available as not a case study identified in the article; U.S = The United States of America; QLD = Queensland, Australia; C = Collective intelligence; L = Location awareness; S = Situation awareness CL = Collective intelligence + Location awareness; CS = Collective intelligence + Situation awareness; LS = Location awareness + Situation awareness; CLS = Collective intelligence + Location awareness + Situation awareness.

lists selected studies.

A few studies conducted analysis covering different attributes, while most of the selected literature focuses on one attribute. A study, for example [46], focused on only the identification of the location of the victims by using social media data according to the pattern of a hurricane, which refers to location awareness, while another study, for example [47], suggested the simultaneous evolution of the effects of the pattern and the severity of the hurricane, which refers to situation awareness. Because the latter case tends to create a disaster severity mapping methodology using social media data in which the location information is coupled with the damage severity assessment, the study extends its attributes beyond to location awareness [47,48]. Three attributes have seven possible combinations, and the distribution of the selected articles on each combination is shown in Figure 3. In total, 78 articles (n = 77%) focused on only one attribute whereas 24 articles (n = 23%) combined separate attributes.

Table 3. Combination of attributes with disaster themes and corresponding articles.

Combinations of Attributes	Disaster Themes	References
Collective Intelligence	Accidents	[41,42,49]
	Natural Hazards	[41,42,43,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78]
	Public Health Events	[41,42,79,80,81,82]
	Social Security Events	[41,42,44,83,84,85]
Location awareness	Accidents	[None]
	Natural Hazards	[40,46,86,87,88,89,90]
	Public Health Events	[None]
	Social Security Events	[None]
Situation awareness	Accidents	[3,91,92]
	Natural Hazards	[38,39,45,47,48,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112]
	Public Health Events	[113]
	Social Security Events	[39,114]
CL	Accidents	[None]
	Natural Hazards	[115,116]
	Public Health Events	[None]
	Social Security Events	[None]
CS	Accidents	[117]
	Natural Hazards	[118,119,120,121,122,123,124,125,126]
	Public Health Events	[127]
	Social Security Events	[None]
LS	Accidents	[None]
	Natural Hazards	[128,129,130,131,132]
	Public Health Events	[133]
	Social Security Events	[134,135]
CLS	Accidents	[34]
	Natural Hazards	[34,136]
	Public Health Events	[34]
	Social Security Events	[34,137]

CL = Collective intelligence + Location awareness; CS = Collective intelligence + Situation awareness; LS = Location awareness + Situation awareness; CLS = Collective intelligence + Location awareness + Situation awareness.

lists the articles depending on the attributes and the combinations of the attributes according to disaster themes. It should be noted that the references focusing on more than one case study or scenario were categorized under multiple disaster themes in Table 3. Natural hazards are the dominant disaster theme in the literature regardless of whether the study considers only one attribute or a combination of the attributes.

4.2.1. Collective Intelligence

The utilization of social media to overcome the challenge of collective intelligence in disaster response is at the forefront of the selected literature. This is not surprising because the aim of any crowdsourcing tool is to address the challenges and obstacles in sharing information between different parties, and social media as a crowdsourcing tool is not an exception [13]. The easiest and quickest way to organize constant and prompt crisis communication in disaster response is to provide the volunteer mass information exchange between the parties using the social media tools [28]. Such high interest in providing a collective intelligence for disaster response, especially for disaster response to natural hazards, shows the necessity to organize large number of people through social media platforms to provide a collaborative disaster response.

The literature [49] advocates that both the local volunteers in a disaster zone and digital volunteers can function as boundary spanners; hence, authorities can link the information from the disaster zone to external sources of information as a function of crisis communication. Furthermore, this communication helps the communities to express environmental and economic concerns as well as public frustration towards the authorities [49]. A recent study [50] proposed a study to explore whether demographic dimensions in a disaster-affected community can be understood using social media analytics, and the study concluded that collective intelligence provided by social media data enables the authorities to identify the more sensitive groups in the disaster-affected community. Furthermore, collective effort made by the disaster-affected public to disseminate the information allows the authorities to better understand the public’s on-site needs after the disaster [51]. Similarly, the authorities can lead the disaster-affected public in a quick and prompt way by disseminating the required information to organize the disaster response on-site.

Collective intelligence can act as the control system of disaster response after a disaster by using the text classification systems to control the distribution of the resources needed by the public in the disaster zone [53,54,55,59,60,61,63,68,69,70,72]. If there is any unfairness in the propagation of the resources in disaster response, it can be identified thanks to the collective intelligence provided by the disaster-affected public in the disaster zone [73], and reliability of actions taken for disaster response can also be identified by the participation patterns through the collective intelligence [74,75,76,77].

The current social media platforms including Facebook and Twitter provide the community with built-in collective systems including group applications and hashtags. These built-in collective systems increase interaction, which accelerates the collective intelligence after a disaster [42]. Given that this information flow must be checked to prevent the systems from misleading collaborative information [52], a group of articles have focused on the data quality and improving information dissemination ways to address the problem related to misleading collaborative information. Social media data can provide the community with the safest information dissemination, eliminating the misleading collaborative information by classifying the messages of the messengers [55], detecting misleading information by near-real-time cross-checking the information against other sources [57], and mapping the communication networks [59].

COVID-19 has emerged as a major research topic in many research domains as it is a public health event that affects everyone on the globe without exception. A recent study [79] explored the characteristics of COVID-19 patients using data-mining methods on social media data, and [80] provides a case study to understand the characteristics of TikTok users and the effects of these characteristics on information sharing via social media with regards to COVID-19. Additionally, Refs. [81,82] conducted sentiment analysis to understand public opinion on how health agencies respond to the COVID-19 disaster. The literature shows that social media data can be utilized to not only understand the authorities’ success in disaster response to COVID-19 but also to understand the affected people’s feelings and characteristics.

Large-scale accidents and social security events can have disastrous consequences; hence, they are perceived as man-made disasters in the literature [34]. Ref. [49] aimed to understand the public sentiment after the Deepwater Horizon oil spill, an accident that happened in the Gulf of Mexico in 2010. The study conducted content analysis and sentiment analysis of the content and flow of the tweets to show how the flow accelerated disaster response by providing higher collective intelligence. Similarly, Refs. [44,83,85] used social media analytics to understand how the collective intelligence evolved into the public sentiment in the aftermath of mass shooting events in Kenya, India, and Brussels–Nice–Paris, respectively. In addition, Ref. [84] conducted content and sentiment analysis to evaluate the public opinion on the Syria Chemical attack in 2017. In summary, the public sentiment after a disaster can be captured by collective intelligence in not only social security events but also natural hazards including earthquakes [62], floods [64,65,71], bush fires [78], tsunamis [66], and typhoons [67].

4.2.2. Location Awareness

Disaster response requires accurate spatial information to locate the cascading effects of a disaster and the victims [86]. Conventional technologies to gather location-based information after a disaster, including radar satellites, aerial tools and equipment, and types of aircraft, are prone to atmospheric conditions [86]. Furthermore, these conventional technologies are complex, and they require skilled human resource to operate. These challenges lead to the need for new technologies that have the capacity to capture the human-centric location-based information under any atmospheric condition without requiring skilled human resource. Social media platforms can provide human-centric location-based information intelligence to address the challenges in gathering location-based information data after disasters to provide improved and data-driven disaster response. Furthermore, the human-centric location-based information provided by social media data is accessible to anyone, and it can be used by many parties simultaneously, which accelerates the speed of disaster response [38].

There are two ways to gather human-centric location-based information intelligence via social media data: (a) geo-located data and (b) toponyms [46]. Social media data have proven capacity to provide human-centric location-based data through application programming interfaces (APIs) of social media platforms in the form of geo-located data [87,88]. Additionally, textual information without using any form of geo-located data but using toponyms can be used as volunteer geographic information to identify the specific locations in the disaster zone. As a result, the disaster response teams can identify the specific locations of the cascading effects of the disaster, victims, and on-site needs to provide better disaster response where the geo-located data are not sufficient [46].

The near-real-time mapping of disasters’ footprint through social media data with live updates improves the disaster response time [86]. While open-source mapping platforms such as Google Maps, Open Street Map (OSM), Bing Maps, Yahoo Maps, and map-mashups including Victorian Bushfire Map, Queensland Globe, and Flood Awareness Online can provide the community with near-real-time interactive maps through crowd-mapping, these interactive maps require skilled workforce to process the data and to visualize the location-based information on the interactive maps for the end-users [46,87,88]. Therefore, the propagation speed of location-based information heavily depends on not only the reaction time of the authorities to process the location data but also on the speed of gathering the location data using conventional crowdsourcing tools [87].

On the other hand, the propagation speed of social media provided human-centric location-based information is much faster compared to the propagation speed of location-based information using conventional crowdsourcing tools. Moreover, Ref. [88] advocated that the biggest advantage of the utilization of social media for raising location awareness is that an individual is not required to be physically present in a disaster zone to propagate the location-based information. Indeed, virtual volunteers can disseminate the human-centric location-based information captured by social media without physically observing the disaster zone. It helps to accelerate the speed of disaster response by communicating the human-centric location-based information to a wider community that is not limited to the people who are able to use open-source mapping platforms and map-mashups.

The timeline of the geographic distribution of the distractions caused by a disaster can be tracked by the disaster response teams to differentiate the hotspots from the other locations in each timeline in the disaster zone thanks to social media analytics. Therefore, spatio-temporal data captured via social media are crucial for predicting the mobility in the disaster zones [89]. Furthermore, deep and machine learning algorithms that can process spatio-temporal data captured via social media can provide the disaster response teams with an automated mapping systems that are crucial to near-real-time mapping to project the evolution of the disaster [40]. Network intelligence is another important factor to bring the disaster response teams and disaster-affected public’s on-site needs to the disaster zone, which can be projected by near-real-time mapping thanks to deep learning models that use human-centric location-based information gathered by social media [45].

The avoidance of misleading collaborative information can be achieved by not only collective intelligence but also location awareness [52]. Therefore, near-real-time mapping is crucial to provide improved and data-driven disaster response, which is a time-sensitive process, because any misleading information on the social media platforms during the disaster response can be detected and eliminated by near-real-time mapping. Volunteered geographic information in the geo-located data can provide this safeness by checking the geolocation of the information depending on the spatial properties [86]. Geo-tagged picture messages posted on the social media platforms can identify the disaster zone and the characteristics of the disaster-affected people and compare them against the information [90]. Thus, integrating Geographic Information Systems (GIS) into near-real-time mapping by using social media data will help check the accuracy of the information on the social media platforms [40].

4.2.3. Situation Awareness

Disaster zones are chaotic areas where the constantly changing situations cannot be foreseen by the disaster response teams because the damage assessment of constantly changing situations in these chaotic zones depends on many constantly changing factors [121]. The constantly changing situations in the disaster zones are hard to assess if depending on only conventional data gathering tools including radars and satellites [96]. Furthermore, natural disasters are sudden and catastrophic events that happen in very large spatial areas within a very limited time [98]. Therefore, conventional data gathering tools or conventional crowdsourcing tools are not capable of providing the authorities and the disaster response teams with the spatio-temporal information on risk items they will face in the disaster zones [96,98,99]. Furthermore, remote sensing techniques to gather the data from disaster zones are prone to the atmospheric conditions, which hinders the prompt data gathering from the disaster zones. However, the human-centric on-site near-real-time information on the changing situations in the disaster zones gathered by social media will facilitate prompt assessment of the risk items corresponding to the changing situations in the disaster zones.

Ref. [96] proposed an approach to supplement the satellite images on a volcano with social media data with the purpose of disaster risk prediction to provide improved and data-driven disaster response to the Taal Volcano eruption that happened in the Philippines. The results of the study prove that (a) social media data can be integrated with external data for a quick and cost-efficient disaster damage assessment over a very wide spatial area and (b) situation awareness provided by hybrid data including social media data leads to faster disaster response because the disaster response teams can utilize the real-time statements to identify the ashfall area with the severity after the volcano eruption. Another study [54] compared social media as a crowdsourcing tool to a conventional crowd mapping tool, and the study concluded that social media is superior to the conventional crowd mapping tool in terms of raising situation awareness in a disaster zone because of its capability of gathering the data directly from the disaster-affected population.

In the selected literature, a number of studies tested the potential of the data captured by social media to assess the severity of a disaster. Ref. [94] proposed a new methodology to conduct near-real-time intensity assessment of the disaster-affected public after Typhoon Haiyan. In the study, an index, Normalized Affected Population Index (NAPI), to leverage social media data for the disaster severity assessment was created to provide more timely and accurate disaster information for the disaster response teams. Ref. [98] tested to create a Mercalli intensity scale by using social media data that is used to express the intensity of an earthquake’s damage.

The advantage of using social media data is that it provides the authorities with near-real-time damage assessment whereas a Mercalli intensity scale report can take days to be prepared [98]. The study concluded that the data captured by social media successfully creates a rapid situation awareness in the intensity of the earthquake. Ref. [99] created a conceptual decision-making framework depending on the social media data to create situational awareness for emergency management. The framework was tested in two different natural hazards, and the study concluded that only 2% of the social media data captured during a disaster is enough to create the conceptual decision-making framework for raising situation awareness [99].

The literature [39,113,114] utilized social media to create a near-real-time situation awareness tool for natural hazards, social security events, and public health events, respectively. Ref. [39] utilized social media data for real-time disaster damage assessment for not only an aftermath of a natural hazard, Typhoon Nepartak, but also the aftermath of a social security event, the Tianjin explosion. Ref. [114] created a framework for people who were at the airport during the Fort Lauderdale Hollywood airport shooting. Ref. [113] created a new dissemination pipeline as an alternative model channel to provide the society with situation awareness during Ebola. These studies concluded that the social media platforms can be successfully utilized as near-real-time situation awareness tools to inform people in the disaster zones while the disaster is happening.

Ref. [100] utilized textual data for near-real-time damage assessment by defining a text-based rapid damage assessment framework for an earthquake aligning with Ridgecrest earthquake sequences; Ref. [101] utilized location data captured by social media to prepare a novel model, which is called the spatial logistic growth model, to evaluate the spatial growth of citizen-sensor data after an earthquake. These studies prove that social media data can be combined with external damage assessment indexes or create an individual index to assess the intended objective function in terms of damage assessment to create near-real-time rapid damage assessment models.

The near-real-time damage assessment of flooding can be conducted based on different parameters using the data captured by social media, and the flooded area can be monitored after the flooding depending on the same data and the same parameters to provide a constant assessment for the constantly changing situations in the flooded areas. Ref. [104] utilized social media data to evaluate flood inundation probability, and [105] proposed a model for waterlogging using social media data. Ref. [106] harvested social media data for flood map generation, while [47] created flood severity map using social media data combining images with text. Social media data that contain two different types of data, namely images and texts, increase the accuracy of damage assessment models for flood damage assessment [109].

5. Discussion

5.1. Findings and Insight

The study investigated the potential of social media utilization for disaster response to address the challenges in disaster response. The study offered a novel contribution to the literature by unfolding the potential of social media analytics in disaster response, extending the notion of social media being only a two-way communication tool in disaster response. The key findings of the study help improve the understanding of the current state of the literature and propose a future research agenda. The increasing academic interest in the utilization of social media for improved and data-driven disaster response in the last decade is expected to continue due to the increasing popularity of deep learning and machine learning algorithms that can analyse the unstructured data and process different data types such as texts and images simultaneously.

The utilization of social media for disaster response to natural hazards is the dominant research theme in the selected literature, while the number of studies that utilize social media analytics to address the challenges in disaster response to man-made disasters is increasing. The human-centric near-real-time information provided by social media has already become an integral part of disaster response, which is reflected in 91 case studies conducted focusing on the disasters that happened in the last decade. Considering this fact, social media has the potential of becoming a major alternative data pipeline to the conventional crowdsourcing tools for disaster response that can be used by not only government agencies, emergency authorities, and disaster response teams but also individuals in their daily lives.

Social media platforms are publicly available and usually free of charge platforms, which makes them widely adopted by individuals. This wide adaption of social media platforms leads to massive amount of information exchange, which will facilitate gathering the human-centric information from multiple parties and citizen sensors, disseminating the information to multiple parties, and storing the data in their data banks. The APIs of the social media platforms enable the researchers, data analysts, and emergency managers to harvest and to analyse the data [91]. Twitter is the most used social media platform in the selected literature because of its wide adoption by individuals and free API that provides the researchers with the free data [91].

5.2. Challenges, Opportunities, and Practice Implications

The challenges in disaster response stem from the fact that disasters are sudden, time-limited, and catastrophic events that usually affect multiple locations with different severity. In other words, disasters have spatial and temporal boundaries, and the challenges in disaster response are related to information dissemination from the disaster zones within a very limited time. In many cases, conventional data-gathering technologies including remote sensing techniques, radars and satellites, and conventional crowdsourcing tools including open-source mapping platforms and map-mashups are not capable of disseminating the information in a prompt way for data-driven disaster response. On the other hand, human-centric information gathered by social media platforms can be disseminated from multiple parties to multiple parties in a prompt way, which improves the disaster response. Furthermore, it is the promptest way to gather the information from the disaster zones and disseminate the information because the human-centric information gathered by social media is not prone to atmospheric conditions, and the dissemination of the information does not require a skilled workforce.

The utilization of social media to address the challenge of organizing a constant and prompt crisis communication for disaster response leads to collective intelligence provided by social media for disaster response. Through the near-real-time information exchange in collective intelligence during the disaster response, collective intelligence enables emergency managers and disaster response teams (a) to understand the public opinion about disaster response and the social dimensions of the disaster-affected public, (b) to eliminate the misleading collaborative information dissemination during disaster response, and (c) to monitor the propagation of the disaster response actions and the resources needed by the public in disaster zones.

The challenge in gathering location-based information from the disaster zones by using conventional methods can be overcome through adopting human-centric location-based information intelligence. This intelligence, location awareness, is provided by social media platforms in the form of geo-located data and toponyms. Given that geo-located data might be insufficient, as shown in some of the selected literature, toponyms have been utilized as volunteer geographic information by many researchers using various text classification techniques and deep learning algorithms. Location awareness provides emergency managers and disaster response teams with the information on the specific locations of cascading effects of a disaster, victims, and on-site needs in the disaster zone. Consequently, (a) the near-real-time mapping of disasters’ footprint, (b) identification of the hotspots in the disaster zones with regards to spatial-temporal properties of a disaster, and (c) detection of collaborative misleading information on social media platforms are the sub-challenges in disaster response that can be achieved through location awareness.

The damage assessment of constantly changing situations in disaster zones is hard to conduct, which hinders the emergency managers and disaster response teams from being provided with the information about on-site risk items in the disaster zones. The human-centric on-site near-real-time information on the constantly changing situations in the disaster zones gathered by social media can provide situation awareness. The situation awareness enables the researchers (a) to combine the data captured by social media with external supplement data in order to create near-real-time damage assessment data pipelines, (b) to create near-real-time damage severity mapping by utilizing the real-time statements, (c) to create novel models for damage assessment that aligns with the existing damage assessment frameworks, and (d) to monitor the situation after a disaster, creating a near-real-time situation awareness tool that is capable of assessing the constantly changing situations in disaster zones depending on constantly changing parameters.

6. Conclusions

6.1. Research and Future Outlook

Despite the growing popularity of social media platforms in our daily lives, there is, unfortunately, no systematic implications of social media analytics in the disaster response. Despite a very wide range of applications in different disciplines including natural hazards such as flooding, earthquakes, tornados; public heath events such as COVID-19; accidents and social security events including mass shootings, no previous scholarly published work has been conducted considering all these perspectives. This research fulfills this research gap.

The fast-growing social media analytics literature is heading toward identifying the potential of deep learning and machine learning algorithms that support the social media analytics in disaster response. This potential is expected to inform disaster managers, engineers who design water and transport infrastructures, and urban planners to plan better disaster response activities considering the different perspectives of disaster response. Therefore, the decision-making processes, disaster response guidelines, infrastructure design guidelines, and urban planning processes can be assessed using social media analytics according to the previous disasters.

The study suggests that future research is need for an improved and data-driven disaster response by utilizing the data captured by social media platforms. In the selected literature, Ref. [3] created a framework to a near-real-time damage assessment for road damage scenarios, combining textual data with images; Ref. [91] utilized social media data to assess near-real-time traffic incidents that might cause an emergency, and Ref. [92] utilized social media data to evaluate near-real-time traffic infrastructures’ damages. While the transport network intelligence is crucial in delivering the disaster response teams to disaster zones, evacuating the victims from disaster zones, and delivering the on-site needs to disaster zones, there is limited research focusing on near-real-time transport network intelligence management considering the transport network performance losses.

Furthermore, as the travel time changes after a disaster due to the constantly changing new conditions on the road segments and failure of the critical infrastructures in a transport network, future research on near-real-time transport network intelligence management utilizing human-captured information provided by social media is needed to improve the disaster response. For instance, in the selected literature, many studies focus on flood mapping using social media analytics after a flooding or typhoon. However, these studies failed to map the distractions in the transport network caused by the flooding that affect the accessibility index of the locations, the most needed by the public after a disaster, including hospitals. Therefore, the near-real-time transport network intelligence methods and techniques after disasters need to be improved to build more sustainable cities, improving the resilience of cities against disasters, especially natural hazards. For future research direction, this urgent need in the research domain can be filled with a design-led qualitative research approach.

6.2. Limitations and Future Directions

The study has the following limitations: (a) conference proceedings, book chapters, and white papers were excluded; (b) selected search keywords could have omitted some relevant articles; (c) authors’ unconscious bias could have an influence on the findings; (d) although the paper covered disaster response and disaster risk reduction, the review did not specifically focus on these areas; (e) the methodology was a manual literature review technique and did not include techniques such as cognitive mapping and concept clustering. Despite these limitations, the research results shed light for the way forward to provide better understanding of the current literature and potential of social media in disaster response for a future research agenda.