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].

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].

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].