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Acikara, T.; Xia, B.; Yigitcanlar, T.; Hon, C. Social Media Analytics for Disaster Response Effectiveness. Encyclopedia. Available online: (accessed on 17 June 2024).
Acikara T, Xia B, Yigitcanlar T, Hon C. Social Media Analytics for Disaster Response Effectiveness. Encyclopedia. Available at: Accessed June 17, 2024.
Acikara, Turgut, Bo Xia, Tan Yigitcanlar, Carol Hon. "Social Media Analytics for Disaster Response Effectiveness" Encyclopedia, (accessed June 17, 2024).
Acikara, T., Xia, B., Yigitcanlar, T., & Hon, C. (2023, June 09). Social Media Analytics for Disaster Response Effectiveness. In Encyclopedia.
Acikara, Turgut, et al. "Social Media Analytics for Disaster Response Effectiveness." Encyclopedia. Web. 09 June, 2023.
Social Media Analytics for Disaster Response Effectiveness

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. 

collective intelligence crisis communication crowdsourcing data analytics disaster response

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.

2. 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 [8]. 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 [9]. 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 [10] 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 [10]. A recent study [11] 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 [12]. 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 [13][14][15][16][17][18][19][20][21][22][23]. 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 [24], and reliability of actions taken for disaster response can also be identified by the participation patterns through the collective intelligence [25][26][27][28].
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 [29]. Given that this information flow must be checked to prevent the systems from misleading collaborative information [30], 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 [15], detecting misleading information by near-real-time cross-checking the information against other sources [31], and mapping the communication networks [16].
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 [32] explored the characteristics of COVID-19 patients using data-mining methods on social media data, and [33] 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. [34][35] 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 [36]. Ref. [10] 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. [37][38][39] 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. [40] 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 [41], floods [42][43][44], bush fires [45], tsunamis [46], and typhoons [47].

3. Location Awareness

Disaster response requires accurate spatial information to locate the cascading effects of a disaster and the victims [48]. 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 [48]. 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 [49].
There are two ways to gather human-centric location-based information intelligence via social media data: (a) geo-located data and (b) toponyms [50]. 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 [51][52]. 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 [50].
The near-real-time mapping of disasters’ footprint through social media data with live updates improves the disaster response time [48]. 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 [50][51][52]. 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 [51].
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. [52] 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 [53]. 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 [54]. 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 [55].
The avoidance of misleading collaborative information can be achieved by not only collective intelligence but also location awareness [30]. 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 [48]. 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 [56]. 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 [54].

4. 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 [57]. The constantly changing situations in the disaster zones are hard to assess if depending on only conventional data gathering tools including radars and satellites [58]. Furthermore, natural disasters are sudden and catastrophic events that happen in very large spatial areas within a very limited time [59]. 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 [58][59][60]. 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. [58] 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 [14] 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. [61] 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. [59] 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 [59]. The study concluded that the data captured by social media successfully creates a rapid situation awareness in the intensity of the earthquake. Ref. [60] 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 [60].
The literature [62][63][64] utilized social media to create a near-real-time situation awareness tool for natural hazards, social security events, and public health events, respectively. Ref. [62] 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. [64] created a framework for people who were at the airport during the Fort Lauderdale Hollywood airport shooting. Ref. [63] 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. [65] 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. [66] 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. [67] utilized social media data to evaluate flood inundation probability, and [68] proposed a model for waterlogging using social media data. Ref. [69] harvested social media data for flood map generation, while [70] 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 [71].


  1. Klomp, J. Economic development and natural disasters: A satellite data analysis. Glob. Environ. Chang. 2016, 36, 67–88.
  2. Hagg, E.; Dahinten, V.S.; Currie, L.M. The emerging use of social media for health-related purposes in low and middle-income countries: A scoping review. Int. J. Med. Inform. 2018, 115, 92–105.
  3. Rashid, M.T.; Zhang, D.Y.; Wang, D. DASC: Towards A Road Damage-Aware Social-Media-Driven Car Sensing Framework for Disaster Response Applications. Pervasive Mob. Comput. 2020, 67, 101207.
  4. Zhang, C.; Yao, W.; Yang, Y.; Huang, R.; Mostafavi, A. Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters. Comput. Civ. Infrastruct. Eng. 2020, 35, 1331–1348.
  5. Yigitcanlar, T.; Butler, L.; Windle, E.; Desouza, K.C.; Mehmood, R.; Corchado, J.M. Can building “artificially intelligent cities” safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors 2020, 20, 2988.
  6. Sarcevic, A.; Palen, L.; White, J.; Starbird, K.; Bagdouri, M.; Anderson, K. Beacons of Hope in Decentralized Coordination: Learning from On-the-Ground Medical Twitterers During the Haiti 2010 Earthquake; CSCW; ACM: Seattle, DC, USA, 2012.
  7. Yigitcanlar, T.; Kankanamge, N.; Vella, K. How Are Smart City Concepts and Technologies Perceived and Utilized? A Systematic Geo-Twitter Analysis of Smart Cities in Australia. J. Urban Technol. 2021, 28, 135–154.
  8. Bharosa, N.; Lee, J.; Janssen, M. Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: Propositions from field exercises. J. Inf. Syst. Front. 2010, 12, 49–65.
  9. Alam, T.; Imran, M.; Ofli, F. Robust Training of Social Media Image Classification Models for Rapid Disaster Response. arXiv 2021, arXiv:2104.04184.
  10. Beedasy, J.; Zúñiga, A.F.S.; Chandler, T.; Slack, T. Online community discourse during the Deepwater Horizon oil spill: An analysis of Twitter interactions. Int. J. Disaster Risk Reduct. 2020, 51, 101870.
  11. Yuan, F.; Li, M.; Liu, R.; Zhai, W.; Qi, B. Social media for enhanced understanding of disaster resilience during Hurricane Florence. Int. J. Inf. Manag. 2021, 57, 102289.
  12. Yeo, J.; Knox, C.C.; Jung, K. Unveiling cultures in emergency response communication networks on social media: Following the 2016 Louisiana floods. Qual. Quant. 2017, 52, 519–535.
  13. Comunello, F.; Parisi, L.; Lauciani, V.; Magnoni, F.; Casarotti, E. Tweeting after an earthquake: User localization and communication patterns during the 2012 Emilia seismic sequence. Ann. Geophys. 2016, 59, S0537.
  14. McClendon, S.; Robinson, A.C. Leveraging Geospatially-Oriented Social Media Communications in Disaster Response. Int. J. Inf. Syst. Crisis Response Manag. 2013, 5, 22–40.
  15. Purohit, H.; Hampton, A.; Bhatt, S.; Shalin, V.L.; Sheth, A.P.; Flach, J.M. Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination. Comput. Support. Coop. Work 2014, 23, 513–545.
  16. Fan, C.; Jiang, Y.; Yang, Y.; Zhang, C.; Mostafavi, A. Crowd or Hubs: Information diffusion patterns in online social networks in disasters. Int. J. Disaster Risk Reduct. 2020, 46, 101498.
  17. Korolov, R.; Peabody, J.; Lavoie, A.; Das, S.; Magdon-Ismail, M.; Wallace, W. Predicting charitable donations using social media. Soc. Netw. Anal. Min. 2016, 6, 1–10.
  18. Canales, K.L.; Pope, J.; Maestas, C.D. Tweeting Blame in a Federalist System: Attributions for Disaster Response in Social Media Following Hurricane Sandy. Soc. Sci. Q. 2019, 100, 2594–2606.
  19. Kim, J.; Hastak, M. Social network analysis: Characteristics of online social networks after a disaster. Int. J. Inf. Manag. 2018, 38, 86–96.
  20. Tim, Y.; Pan, S.L.; Ractham, P.; Kaewkitipong, L. Digitally enabled disaster response: The emergence of social media as boundary objects in a flooding disaster. Inf. Syst. J. 2017, 27, 197–232.
  21. Zhang, T.; Shen, S.; Cheng, C.; Su, K.; Zhang, X. A topic model based framework for identifying the distribution of demand for relief supplies using social media data. Int. J. Geogr. Inf. Sci. IJGIS 2021, 35, 2216–2237.
  22. Lai, C.-H.; She, B.; Tao, C.-C. Connecting the dots: A longitudinal observation of relief organizations’ representational networks on social media. Comput. Hum. Behav. 2017, 74, 224–234.
  23. Albris, K. The switchboard mechanism: How social media connected citizens during the 2013 floods in Dresden. J. Contingencies Crisis Manag. 2018, 26, 350–357.
  24. Zhang, C.; Yang, Y.; Mostafavi, A. Revealing Unfairness in social media contributors’ attention to vulnerable urban areas during disasters. Int. J. Disaster Risk Reduct. 2021, 58, 102160.
  25. Ogie, R.I.; Forehead, H.; Clarke, R.J.; Perez, P. Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management. Inf. Syst. Front. 2017, 20, 713–728.
  26. Lai, C.-H. A study of emergent organizing and technological affordances after a natural disaster. Online Inf. Rev. 2017, 41, 507–523.
  27. Kim, J.; Bae, J.; Hastak, M. Emergency information diffusion on online social media during storm Cindy in U.S. Int. J. Inf. Manag. 2018, 40, 153–165.
  28. Camama, A.R.; Baldelomar, J.M.; Claveria, J.M.; Espinas, M.C.; Cabardo, J.M.; Rabago, L.W. Q-DAR: Quick disaster aid and response model using Naïve Bayes and Bag-of-Words algorithm. IOP Conf. Series. Mater. Sci. Eng. 2019, 482, 12051.
  29. Mehta, A.M.; Bruns, A.; Newton, J. Trust, but verify: Social media models for disaster management. Disasters 2017, 41, 549–565.
  30. Lian, Y.; Liu, Y.; Dong, X. Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China. Technol. Soc. 2020, 62, 101265.
  31. Cheong, S.-M.; Babcock, M. Attention to misleading and contentious tweets in the case of Hurricane Harvey. Nat. Hazards 2020, 105, 2883–2906.
  32. Huang, C.; Xu, X.; Cai, Y.; Ge, Q.; Zeng, G.; Li, X.; Zhang, W.; Ji, C.; Yang, L. Mining the characteristics of COVID-19 patients in China: Analysis of social media posts. J. Med. Internet Res. 2020, 22, e19087.
  33. Xu, D.; Yan, S.; Zhang, Y.; Zhang, S.; Nakamori, Y.; Chen, L. Knowledge management for extreme public health events COVID-19: Based on Tiktok data. J. Knowl. Manag. 2021, 26, 2354–2369.
  34. Rahmanti, A.R.; Ningrum, D.N.A.; Lazuardi, L.; Yang, H.-C.; Li, Y.-C. Social Media Data Analytics for Outbreak Risk Communication: Public Attention on the “New Normal” During the COVID-19 Pandemic in Indonesia. Comput. Methods Programs Biomed. 2021, 205, 106083.
  35. Kothari, A.; Foisey, L.; Donelle, L.; Bauer, M. 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 2021, 11, e041818.
  36. Li, S.; Liu, Z.; Li, Y. Temporal and spatial evolution of online public sentiment on emergencies. Inf. Process. Manag. 2020, 57, 102177.
  37. Simon, T.; Goldberg, A.; Aharonson-Daniel, L.; Leykin, D.; Adini, B. Twitter in the cross fire—The use of social media in the Westgate mall terror attack in Kenya. PLoS ONE 2014, 9, e104136.
  38. Karmegam, D.; Mappillairaju, B. 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. Inf. Dev. 2021, 15, 26666692110491.
  39. Santoni, V.; Rufat, S. How fast is fast enough? Twitter usability during emergencies. Geoforum 2021, 124, 20–35.
  40. Bashir, S.; Bano, S.; Shueb, S.; Gul, S.; Mir, A.A.; Ashraf, R.; Shakeela; Noor, N. Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack. Int. J. Disaster Risk Reduct. 2021, 62, 102397.
  41. Gray, B.; Weal, M.J.; Martín, D. Social media during multi-hazard disasters: Lessons from the KAIKOURA EARTHQUAKE 2016. Int. J. Saf. Secur. Eng. 2017, 7, 313–323.
  42. Han, X.; Wang, J. Using social media to mine and analyze public sentiment during a disaster: A case study of the 2018 Shouguang city flood in China. ISPRS Int. J. Geo-Inf. 2019, 8, 185.
  43. Brandt, H.M.; Turner-McGrievy, G.; Friedman, D.B.; Gentile, D.; Schrock, C.; Thomas, T.; West, D. Examining the Role of Twitter in Response and Recovery during and after Historic Flooding in South Carolina. J. Public Health Manag. Pract. 2019, 25, E6–E12.
  44. Cheng, X.; Han, G.; Zhao, Y.; Li, L. Evaluating social media response to urban flood disaster: Case study on an East Asian City (Wuhan, China). Sustainability 2019, 11, 5330.
  45. Abedin, B.; Babar, A. Institutional vs. Non-institutional use of Social Media during Emergency Response: A Case of Twitter in 2014 Australian Bush Fire. Inf. Syst. Front. 2017, 20, 729–740.
  46. Salsabilla, F.P.; Hizbaron, D.R. Understanding Community Collective Behaviour Through Social Media Responses: Case of Sunda Strait Tsunami, 2018, Indonesia. E3S Web Conf. 2021, 325, 01021.
  47. Wu, K.; Wu, J.; Ding, W.; Tang, R. Extracting disaster information based on Sina Weibo in China: A case study of the 2019 Typhoon Lekima. Int. J. Disaster Risk Reduct. 2021, 60, 102304.
  48. Shankar, A.R.; Fernandez-Marquez, J.L.; Pernici, B.; Scalia, G.; Mondardini, M.R.; Serugendo, G. Crowd4Ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2019, 42, 331–340.
  49. Asif, A.; Khatoon, S.; Hasan, M.; Alshamari, M.A.; Abdou, S.; Elsayed, K.M.; Rashwan, M. Automatic analysis of social media images to identify disaster type and infer appropriate emergency response. J. Big Data 2021, 8, 1–28.
  50. Grace, R. Toponym usage in social media in emergencies. Int. J. Disaster Risk Reduct. 2021, 52, 101923.
  51. Banerjee, S.; Hosack, B.; Lim, B.; Kostelnick, J. Social Media Widget For Emergency Response. Issues Inf. Syst. 2013, 14, 289–297.
  52. Athanasis, N.; Themistocleous, M.; Kalabokidis, K.; Papakonstantinou, A.; Soulakellis, N.; Palaiologou, P. The emergence of social media for natural disasters management: A big data perspective. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2018, 42, 75–82.
  53. Yabe, T.; Rao, P.S.C.; Ukkusuri, S.V. Modeling the influence of online social media information on post-disaster mobility decisions. Sustainability 2021, 13, 5254.
  54. Fan, C.; Wu, F.; Mostafavi, A. A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters. IEEE Access 2020, 8, 10478–10490.
  55. Chaudhuri, N.; Bose, I. Exploring the role of deep neural networks for post-disaster decision support. Decis. Support Syst. 2020, 130, 113234.
  56. Kent, J.D.; Capello, H.T., Jr. Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012. Cartogr. Geogr. Inf. Sci. 2013, 40, 78–89.
  57. Fang, J.; Hu, J.; Shi, X.; Zhao, L. Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm. Int. J. Disaster Risk Reduct. 2019, 34, 275–282.
  58. Yute, A.L.F.; Merin, E.J.G.; Sarmiento, C.J.S.; Elazagui, E.E. Supplementing Satellite Imagery With Social Media Data For Remote Reconnaissance: A Case Study Of The 2020 Taal Volcano Eruption. ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 2021, XLVI-4/W6-2021, 329–335.
  59. Mendoza, M.; Poblete, B.; Valderrama, I. Nowcasting earthquake damages with Twitter. EPJ Data Sci. 2019, 8, 1–23.
  60. Freitas, D.P.; Borges, M.R.S.; De Carvalho, P.V.R. A conceptual framework for developing solutions that organise social media information for emergency response teams. Behav. Inf. Technol. 2020, 39, 360–378.
  61. Cheng, C.; Zhang, T.; Su, K.; Gao, P.; Shen, S. Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data. ISPRS Int. J. Geo-Inf. 2019, 8, 358.
  62. Shan, S.; Zhao, F.; Wei, Y.; Liu, M. Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter). Saf. Sci. 2019, 115, 393–413.
  63. Fung, I.C.-H.; Fu, K.-W.; Chan, C.-H.; Chan, B.S.B.; Cheung, C.-N.; Abraham, T.; Tse, Z.T.H. Social Media’s Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors. Public Health Rep. 2016, 131, 461–473.
  64. Bateman, G.; Haleem, H.A.; Majumdar, A. 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 Stud. Transp. Policy 2021, 9, 1015–1025.
  65. Li, L.; Bensi, M.; Cui, Q.; Baecher, G.B.; Huang, Y. Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event. Int. J. Inf. Manag. 2021, 60, 102378.
  66. Wang, Y.; Ruan, S.; Wang, T.; Qiao, M. Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data. Int. J. Digit. Earth 2019, 12, 1265–1284.
  67. Huang, X.; Wang, C.; Li, Z. Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery With Real-Time Gauges and Tweets. IEEE Trans. Geosci. Remote Sens. 2018, 56, 4691–4701.
  68. Xu, L.; Ma, A. Coarse-to-fine waterlogging probability assessment based on remote sensing image and social media data. Geo-Spat. Inf. Sci. 2021, 24, 279–301.
  69. Eilander, D.; Trambauer, P.; Wagemaker, J.; van Loenen, A. Harvesting Social Media for Generation of Near Real-time Flood Maps. Procedia Eng. 2016, 154, 176–183.
  70. Feng, Y.; Brenner, C.; Sester, M. Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey. ISPRS J. Photogramm. Remote Sens. 2020, 169, 301–319.
  71. Alam, F.; Ofli, F.; Imran, M. Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behav. Inf. Technol. 2020, 39, 288–318.
Subjects: Engineering, Civil
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Update Date: 12 Jun 2023
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