In a hazard scenario, Volunteered Geographic Information (VGI) through social media is advantageous, but due to lack of reliability and increased generation of data, rapid decision-making is affected . Considering these issues, we have derived the following research questions.

Q1: Do exclusion criteria assist in relevance filtering of SMD?

In the context of large and noisy social media datasets, exclusion criteria serve as initial filters to eliminate irrelevant or misleading content. These criteria typically involve keywords or topic filters used to pre-process the data before applying more advanced methods. While technical in nature, this process is a foundational step in any meaningful analysis of SMD, particularly in domains like disaster response. Our review identifies and analyses literature that applies exclusion-based techniques, such as rule-based filters, Natural Language Processing (NLP) models, or Machine Learning (ML) algorithms, to enhance relevance in data collection. These methods are widely applicable across domains, not just in disaster management, and are crucial for practitioners who engage with unstructured, real-time SMD.

To construct the Social Media Literature Database (SMLD) , we searched Google Scholar and Scopus using keywords related to “disaster management” and “data analysis”, forming five Boolean search strings applied to publication titles (Fig. ). Each search yielded publications (nr), from which relevant ones (ni) were manually selected from peer-reviewed journals, conferences, and reports (January 2010–September 2023).

Our search used only the keyword “Twitter” to represent social media, omitting platforms like Facebook, Instagram, TikTok, and Weibo. This platform-specific focus reflects broader trends in literature due to Twitter's accessible Application Programming Interface (API). Nonetheless, around 5 % of publications also discussed other platforms (Sects.  and ).

During Phase I, we screened titles using disaster-related Boolean combinations and reviewed abstracts for relevance. However, some relevant studies were missed due to unmatched keyword variations. For instance, a key article by was excluded despite being retrieved using (“Social Media” AND “Natural Hazard*”), a test query that yielded thirty publications, of which seven were relevant, but only one matched our original search.

Examples of relevant but missed studies include “Rapid Flood Inundation Mapping using Social Media, Remote Sensing and Topographic Data” , “Sub-Event Discovery and Retrieval during Natural Hazards on Social Media Data” , “Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories” , and “Public Attention to Natural Hazard Warnings on Social Media in China” .

After inclusion keyword searches were done, and publications that did not match our focus area of research were removed, our critical literature review resulted in 250 publications which were included in our Social Media Literature Database. Future reviews might iteratively refine keyword strategies to improve coverage and reduce bias.

We defined seven major categories, and 27 sub-categories for our Social Media Literature Database (Fig. ). For each of the 250 publications, we identified information that could be assigned to these seven categories and their respective categories. In addition to these data, we also conducted an actionable information analysis of the 250 publications, as detailed in Sect. . We briefly describe the seven major categories here: A.

“Article Description” describes the metadata, such as the author and publication details.

Detailed descriptions of all SMLD subcategories are provided in the Appendix (Table  and Fig. ).

In the following section, we describe results of further analyses of the SMLD .

Figure  provides an overview of the total number of publications in SMLD and the total number of citations per year from January 2010 to September 2023. Approximately 90 % of the publications were sourced from Google Scholar, with the remaining 10 % obtained from Scopus.

Over the past decade, many authors have conducted experiments in SMD collection and analysis as depicted in Fig. . Initially, while the number of publications was relatively low, there were a significant number of citations. However, in the SMLD, we observe a substantial increase in both publications and citations from 2014 to 2018. During the last 10 years, a wide range of publications, including journals, conference proceedings, reports, and book chapters, have been published due to the growing use of web data in various phases of the disaster management cycle.

Our critical review encompasses not only peer-reviewed journal articles, but also conference proceedings, reports, and book series chapters. This choice is driven by the fact that these sources often provide insights into the development of SMD collection, which includes filtering, a core aspect of our review. Figure  illustrates the distribution of publications among the categories: “Journal”, “Conference”, “Report”, and “Book”, with the majority of publications falling under the “Journal” category. The year 2018 had the maximum of journals and conference publications. Reports and book chapters are comparatively fewer but provide insights into data collection and analysis strategies.

Over the past decade, researchers have extensively explored the role of data in disaster management, with a growing focus on SMD for collection, analysis, and decision-making support. This section provides an overview of early works, including case studies, methodological publications, and review publications, and outlines the current state of research on the use of SMD in disaster contexts.

We identified four major categories of literature that utilise User-Generated Information (UGI) for disaster management, with the focus and application of each category evolving over time. I.

Surveys and Questionnaires. These studies collect UGI directly from disaster-affected communities through surveys and interviews to assess preparedness and estimate damages . Post-disaster surveys serve as reliable sources for informing mitigation efforts, involving both citizens and officials . Questionnaire surveys were typically conducted among local residents, officials, and school authorities to assess disaster awareness, inform mitigation strategies, and estimate damages based on firsthand accounts of impacts and preventive measures. publications argue that such data is often more credible than social media content, which may contain misinformation .

Figure  visualises the evolution of these categories. Survey-based approaches (Category I) were more prevalent until 2011, after which focus shifted toward social media (Categories II, III, and IV). The literature consistently affirms the utility of social media in disaster contexts, while also acknowledging concerns over data reliability. Some publications span multiple categories, covering both conceptual potential and practical application.

To further understand the current technological trends, we examined the publications that explored the technical aspects of SMD collection and analysis. Since 2010, several authors have employed advanced methods for identifying, acquiring, filtering, and analysing relevant data . Around 95 % of the publications in our database relied on Twitter (X), with the remaining 5 % using other sources like Facebook, Weibo, or manual surveys. This Twitter (X) bias largely stems from search queries emphasising the term “Twitter” (see Fig. ) and is influenced by its greater accessibility and data availability (Sect. ), limiting representativeness across all platforms.

Several authors experimented SMD collection methodologies where they developed frameworks to query the Twitter Streaming API using independent search jobs, storing results in structured databases. These tools allow keyword-, user-, location-, and date-specific queries, proving especially useful in disaster scenarios requiring precise, location-based data .

To further analyse SMD, several studies experimented with emerging technologies such as NLP and AI . These works applied sentiment analysis and feature extraction, particularly from adjectives in tweets, to detect public opinion and emotional tone using probabilistic models like Naive Bayes and Maximum Entropy. Such analysis is valuable for assessing community response and needs during or after disasters .

Several studies examined the behaviour of social media users involved in sharing and consuming disaster-related news, offering insights into user activity patterns and retweet behaviours on platforms like Twitter (X) . Such behavioural analyses help reveal how information spreads during crises and support the development of effective communication strategies . Additionally, researchers highlighted the importance of analysing language use in social media, particularly non-English content, to improve global and community-level disaster response . These works emphasised that linguistic variation, such as local grammar and usage, requires adaptable ML and NLP techniques to extract actionable insights across diverse language contexts.

We also examined several survey and review publications within the literature database that offered critical insights into the methodologies, opportunities, and challenges associated with using SMD across different phases of disaster management.

In the late 2010s, a seminal work by explored data integration, information extraction, filtering, mining, and decision support methods in disaster management. Early contributions stressed the need for a disaster management dataspace and highlighted related challenges. Simultaneously, reviewed the development of risk and crisis management processes to support community engagement in decision-making.

Several authors emphasise the potential of social media to enhance community interaction across all disaster management phases . A pivotal work by explores the roles of the community and organisations in disaster management, on how the public not only seeks life-saving information but can also contribute to effective information dissemination, fostering community awareness. Additionally, the authors critically review how first responder organisations increasingly rely on SMD to identify areas in need of assistance during crises.

Recent reviews have examined advanced data acquisition and preparation techniques, including API calls, querying, and pre-processing . These studies also address geolocation and geocoding for identifying disaster zones. Notably, provided early insights into event detection using such methods.

Key challenges identified include data quality and credibility, particularly in the context of relevance for disaster response . Concerns about misinformation, such as rumours and false data, are prevalent . discusses the risks associated with data from untrained individuals with diverse agendas and expertise, emphasising the lack of quality assurance, and warned of risks posed by unverified sources in SMD, and highlighted the danger of delayed official responses.

investigated the impact of misleading content (e.g., spam, bots, rumours), stressing the importance of filtering such data. The study also noted how user language shifts under distress and recommended probabilistic topic modeling, such as LDA, to detect underlying themes.

Other recent reviews explored AI applications in disaster contexts, especially the analysis of multimodal SMD (text, images, videos, metadata), which can collectively enhance crisis understanding . In a pivotal work by , the article highlights that the multimodal nature of SMD, when collectively analysed, can significantly enhance the understanding of a crisis.

Bibliometric studies by and showed that SMD research gained stability between 2015 and 2019, with NLP, ML, and computer vision emerging as prominent themes. While social media enhances community engagement during crises , its practical use remains constrained by concerns over data reliability and credibility .

To address our research question “Does social media provide actionable information (A-Info) in disaster scenarios?”, we analyse the publications listed in the Social Media Literature Database under the theme of “Disaster Management”. By using various studies and based on our experience, we have defined nine generic Actionable Information (A-Info) categories which are assigned to each publication under the “Disaster management” theme listed in the SMLD.

A publication can fall into one or more of the nine A-Info categories as described in Table . These classifications center around data collection methods, geolocation identification, relevance filtering strategies, community and stakeholder collaborations, and software development. Table  displays the various categories with their respective descriptions, detailing the methods and applications considered within each A-Info category in this study. Additionally, we include references for publications under each A-Info category that have garnered higher citations compared to others in the same category.

Among the 250 publications in the SMLD, 184 were journal articles. Figure  highlights the top five journals (3.0 % of 123 journals) and their article counts (total of 51 journal articles in the top five journals). These articles were classified into three themes: “Disaster Management”, “Social Media Analytics”, and “Social Science”, with “Disaster Management” being the most prominent. Although only two articles fell under “Social Science”, they provide valuable insights into demographic studies using SMD, while “Social Media Analytics” articles focus on data collection techniques from a systems development perspective.

Over 60 % of the publications categorised under the “Disaster Management” theme employed case studies to evaluate their methodologies.

Figure  provides metrics on the use of case studies and their geographical scope. Notably, approximately 50 % of the publications utilised regional case studies, which were the most prevalent among the different geographical scopes. It is worth mentioning that North America, particularly events such as Hurricane Sandy (2012), Hurricane Matthew (2016), and the Red River Valley Flood (2009), was the most frequently used region in these case studies . We can also observe from Figure  that around 39 % of the 250 publications do not use a case study to validate their respective methodologies.

Publications categorised under the theme “Disaster Management” were further classified in the “Event” category, indicating the specific disaster event type (the type of natural hazard) studied by the respective authors. Figure  shows the metrics of the “Event Type” category in the Social Media Literature Database for 175 publications where a hazard type is mentioned and named (removed are n = 75, which includes “Other” event types and “NA” entries). In some studies (n = 10), more than one disaster event was studied. Our examination of these case studies revealed that flood was the most frequently studied hazard type in disasters (n = 47), followed by hurricane (n = 44) . We can also observe that earthquakes as a source of disaster events was studied every year of the review period by authors in our review. The least studied events were storms, volcanoes, and cyclones.

Among the studies listed in the literature database, excluding the review publications, approximately 72 % (182 out of 250) utilised SMD from various platforms as their input data e.g.,. Within this category, 70 % of the studies developed their own methodologies for collecting SMD tailored to their specific needs e.g.,. They frequently employed APIs, such as the Twitter Streaming API and Representational State Transfer (REST) API. The remaining 2 % of the studies utilised SMD available as online resources from various portals e.g.,.

Out of the 250 studies, excluding the review publications, nearly 13 % (34 publications) sourced their data from government authority portals . Frequently accessed portals included FEMA (Federal Emergency Management Agency, USA) and USGS (United States Geological Survey), which offered valuable disaster-related social information, satellite image data, and historical event damage data. Additionally, approximately 4 % (11 publications) of the total used manually collected interview or survey data .

The identification of relevant data presents a significant challenge in SMD collection. The majority of publications, around 70 %, employed NLP-based methods, particularly text analysis, to address this challenge . These methods involved the use of inclusion keywords specific to their topics of interest during data collection. While this approach aids in identifying topic-relevant data, it may also introduce a considerable amount of noise.

The use of exclusionary criteria proved valuable in noise reduction, with approximately 12 % of the publications adopting this approach . These publications utilised NLP and ML-based solutions to exclude irrelevant data. Exclusionary criteria are often constructed based on assumptions, emphasising the need for rigorous evaluation before concluding. However, only a small percentage, approximately 2 % of the publications, conducted such evaluations before proceeding with the data analysis .

In Fig. , we present a summary of the relevance filtering analysis from the publications in the SMLD. We can observe that only 14 % of the 250 publications used exclusionary criteria to perform relevance filtering. Notably, the majority of the publications employed NLP methods to perform filtering in comparison to ML methods. This analysis allowed us to answer our research question (Q1), demonstrating that performing relevance filtering is vital for improving data quality and application effectiveness. We recommend a thorough study of input data and the implementation of NLP or ML methods for effective relevance filtering strategies.

The methodologies employed in the publications within the SMLD encompass a range of techniques in the fields of NLP and ML. These methodologies include text analysis, Named Entity Recognition (NER), Bag-of-Words (BoW), Part-of-speech Tagging (PoS), and various feature extraction methods. Data analysis is carried out using both supervised and unsupervised ML models, employing algorithms such as Logistic Regression (LR), Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbours (k-NN), Convolutional Neural Networks (CNN), Decision Trees (DT), Random Forest (RF), Latent Dirichlet Allocation (LDA), and more .

Some publications employ statistical techniques, including correlation analysis (e.g., Pearson's and Kendall's), distribution analysis (e.g., Poisson and Binomial), and Generalised Additive Models (GAM) . Others explore methodologies that establish relationships among stakeholders in disaster scenarios and conduct network analyses to enhance decision-making in the wake of disasters . Figure  provides metrics on the technologies featured in the reviewed publications.

Figure  shows that NLP methods were employed the most, where text analysis was in the majority. Analysing the text of the social media post helps in identifying topic-relevant keywords, event location, duration of the event, and sentiment of the user. ML methods were also used for analysis, and the SVM algorithm was found frequently used by the investigators. However, neural network algorithms were not used much in the literature duration.

Roughly 65 % of the 250 publications in the SMLD conduct performance evaluations using a range of methods. Publications employing ML algorithms often rely on scoring metrics like accuracy, precision, recall, and F-score e.g.,. Those exploring sentiment analysis in SMD typically utilise polarity scores for evaluation e.g.,. Some publications also employ statistical tests, such as ANOVA, chi-square, correlation values, and invariance tests to validate their methodologies e.g.,. Additionally, a few authors opt for manual evaluations e.g.,.

The publications categorised under the “Disaster Management” theme were categorised further based on the Actionable Information (A-Info, see Table ) classes to address our research question Q2, “Does social media provide actionable information in disaster scenarios?”. Figure  shows the number of publications assigned to each A-Info category by year (2010 to 2023) and on an overall basis, noting that a given study can be categorised in more than one A-Info.

From Fig.  we can observe that the following three A-Info categories were the most prevalent, where studies focused on the development and testing of SMD collection methodologies and geolocation identification methodologies, and conducting spatial analyses:

A-Info-1 “Disaster Data Collection” (45 %; 95 of 211 publications) e.g.,.

Notably, 57 % of the publications were classified under A-Info-3, emphasising the significance of relevance filtering in disaster scenarios, and investigating methods to enhance data quality and reduce noise.

For the other A-Info categories we observed the following:

A-Info-4 “Community Collaborations” (11 %; 25 of 211), which studied how SMD can be utilised for community collaborations e.g.,.

The analysis indicates that current methods, such as NLP and ML, effectively aid in filtering SMD for relevance, reducing noise, and excluding irrelevant content. However, challenges related to data reliability, including rumours and false information, persist. Many data collection methods employ inclusion keywords for relevance, which can introduce noise. The use of exclusion criteria proves valuable in enhancing efficiency by eliminating specific data.

Each study categorised under the “Disaster Management” theme fulfilled at least one A-Info category. Several studies met more than five actionable information categories, demonstrating their valuable contributions to efficient disaster management.

Approximately 70 % of the 250 publications in our Social Media Literature Database employed keyword-based methods for SMD collection, using topic-relevant inclusion terms to extract relevant content . A common challenge in this approach was filtering noise (in other words, false positives, potential social media “hits” which were not relevant).

For example, studies on Hurricane Sandy, a frequently analysed event, used keywords such as “Sandy”, “Hurricane”, “New York”, and “2012” to retrieve related content. However, these also led to irrelevant data like metaphorical phrases (e.g., “hurricane of emotions”) .

To reduce noise, some researchers incorporated exclusion keyword sets. , for instance, removed tweets mentioning “TV shows” during demographic analysis, while filtered disaster-related tweets and news by excluding terms like “Songs”, “Election”, and “Victory” to avoid non-disaster phrases such as “Landslide Victory” (see Sect. ). This approach demonstrates the effectiveness of exclusion keywords in improving data collection efficiency.

Others applied ML techniques, particularly supervised classifiers, to identify relevant posts. However, this required large labelled datasets and domain expertise, making the process resource-intensive .

Our review underscores the utility of exclusion-based filtering in reducing noise (false positives) and improving efficiency. However, it is vital to ensure that such filtering does not omit valuable data. We recommend careful topic analysis and early-stage implementation of exclusion criteria to optimise both time and space complexity in SMD workflows.

Approximately 74 % of the 250 publications in the SMLD under the “Disaster Management” theme used real-world disaster events as case studies to validate their methodologies. Figure  highlights major events frequently examined. These studies often relied on APIs to extract location-specific data (e.g., via bounding boxes) . However, inaccuracies arose when users mentioned non-existent locations. To address this, several works focused on collecting geotagged posts, which better reflect actual user location .

Hurricane Sandy (2012, USA) was the most frequently studied event due to its high social media activity . Several studies addressed misinformation and challenges in content reliability during this event, highlighting the impact of fake content on public perception .

introduced a real-time flood monitoring framework using social media, tested on the 2012 Tyne and Wear flood. For the 2015 Nepal earthquake, proposed a multilingual tweet categorisation approach to identify disaster needs and damages. The 2018 Woolsey fire was also analysed by , focusing on local user behaviour and content.

The events depicted in Fig.  have had significant impacts on the affected populations. To gain a better understanding of the scale of these disasters, we collected and analysed data related to some of the major disasters from EM-DAT, the International Disaster Database maintained by CRED (Centre for Research on the Epidemiology of Disasters), covering the period 2010 to 2023, to assess the number of affected individuals. Figure  presents our findings revealing that the 2012 Hurricane Sandy in the USA and the 2010 China earthquakes (including major events such as the Yushu and Qinghai earthquakes and others in 2010), each affected more than 2 million people.

As demonstrated in Fig.  (refer to Sect. ), our analysis of continent-based case studies revealed that North America was the most frequently utilised region, and it was evident that major disaster events generated more data and garnered increased attention on social media platforms. We recommend increased focus on local disaster events to improve data relevance, manage location ambiguity, and enhance response strategies.

Reliability remains a major concern in leveraging social media for disaster management . To address this, many authors combined social media with external data sources to improve methodological robustness .

External sources included government portals, satellite imagery, disaster statistics, GIS and precipitation data, news reports, and survey/interview data. Among the 250 publications reviewed, 26 % (65 publications) integrated such data, particularly in US-based studies that frequently used FEMA and USGS datasets .

These sources aided not only in supplementing and validating social media-derived insights but also in refining keyword sets and identifying location details often missing from user-generated content . We recommend continued integration of reliable external datasets to improve authenticity and decision-making in disaster response frameworks.

As detailed in Sect.  and illustrated in Fig. , the algorithms used in the reviewed publications were classified into four categories: NLP, ML, Statistical, and Neural Networks. NLP techniques were the most commonly employed, particularly for content analysis during data collection and filtering . Statistical methods supported correlation and distribution analyses .

ML methods gained popularity after 2013 for classification, clustering, and filtering tasks, with SVM, NB, and RF frequently used . Neural network models, though less common, showed promising results in selected applications .

As shown in Fig. , the use of these methods increased after 2015. Researchers often used ML/NN for relevance filtering and disaster event detection in social media content . However, few studies explicitly analysed pre-,during-, and post-event social media posts.

Pre-event posts typically contain early warnings or alerts , while during-event content includes rescue requests and urgent needs . Post-event posts support damage assessment and recovery analysis .

In our recommendations, we emphasise the importance of investigating pre-event posts, as they can provide critical information for early warning systems, helping to save lives and reduce the impact of disasters before they strike a location, contributing to better disaster preparedness and timely responses.

To address research question Q2, “Does social media provide actionable information in disaster scenarios?”, the “Disaster Management” related publications were mapped to nine Actionable Information (A-Info) classes, revealing that every study aligned with at least one class. A-Info-1 (Disaster Data Collection), A-Info-2 (Geolocation Identification and Analysis), and A-Info-3 (Relevance Filtering) were the most frequently addressed, indicating a strong focus on data collection, relevance filtering, and spatiotemporal analysis . In contrast, A-Info-8 (Resource Identification), related to identifying resource needs from social media, was the least explored, reflecting limited attention to during-event classification .

A-Info-7 (Software Development), focusing on real-time platforms for public dissemination, also saw limited research, possibly due to the lack of open-source tools during the review period . Developing such platforms could enhance rapid response and recovery. We recommend that researchers consider creating more platforms or applications for making disaster-relevant data and real-time analysis available to the public.

A-Info-8 (Resource Identification), concerning community interaction analysis, was underrepresented, despite its importance for understanding behavioural dynamics during disaster phases . We recommend that future studies explore these aspects to inform community-based strategies.

Only a few studies addressed five or more A-Info categories (see Table ), primarily focusing on floods and hurricanes. These studies excelled in integrating data filtering, spatial-temporal analysis, and community engagement.

Overall, our analysis highlights the strengths of social media: real-time user content, geolocation, and situational awareness, but also warns of issues like misinformation. Robust filtering and verification mechanisms remain essential. We encourage more focus on A-Info-8 (Resource Identification) and A-Info-9 (Community Response) to support informed, real-time disaster response and resource allocation.

collected disaster-related tweets using inclusion keyword sets. However, further analysis of their data revealed significant noise, tweets containing relevant keywords but unrelated to disasters (e.g., “landslide victory,” “flood of emotions,” “market flooded”). To address this, we developed an exclusion keyword set comprising around 56 exclusionary terms related to elections, music, emotions, and markets.

To evaluate its impact, we sampled 1000 tweets. As shown in Fig. , around 80 % of irrelevant tweets were correctly filtered using four exclusion sets, though some relevant data (approx. 20 %) was missed, especially with music-related filters. Despite this limitation, exclusion criteria proved effective as a first-level filtering approach.

Figure  illustrates a word cloud showing reduced noise and enhanced disaster relevance post-exclusion. While the strategy helps improve data quality, it demands manual curation and periodic updates to adapt to evolving contexts.

In disaster situations, where accuracy is paramount, ML can play a pivotal role in identifying and eliminating outliers and noise. By leveraging both basic NLP and advanced ML, researchers can aspire to achieve a comprehensive strategy for data collection, ensuring that the information extracted from social media during crises is both accurate and actionable.

This section describes seven biases within the Social Media Literature Database publications regarding geographic, methodological, and data-related tendencies. We recognised these biases as the critical review methodology proceeded, solidified through extensive discussions among the authors. By identifying these biases, we aim to enhance the transparency of our analysis and provide a foundation for future research. 1.

Geographic location of the case studies used in disaster-related publications. A notable geographic bias was found in the case studies employed by researchers in the literature, with a predominant focus on North America (see Fig. ). Around 40 % of the studies (60 of 154) used Hurricanes as the case study event, among which around 60 % of the publications used events from North America . This raises concerns about the generalisation of the findings in a global context.

The majority of studies exhibit a bias towards regional and national investigations, overshadowing the importance of local studies (see Fig. ). One of the reasons is that the availability of data is limited from a local scope when compared to a national or regional disaster event. The amount of population that uses social media platforms also varies based on the area scope. Such biases may limit the applicability of findings to specific contexts e.g.,.

Social media has become a vital tool for real-time communication and information dissemination during disasters, supporting the efforts of the public, government, and non-government agencies, volunteers, and other stakeholders in disaster management . As public reliance on these platforms grows, it is essential to establish best practices for both users and researchers to responsibly harness their potential . Drawing from the literature, we propose guidelines for public information sharing and outline strategies for researchers to extract disaster-relevant data. Adopting these practices can enhance disaster response, mitigation, and recovery.

The community plays a crucial role in disaster response by providing valuable information to first responders . Social media platforms are widely utilised for data acquisition during disasters, but the major challenge is to identify reliable information . Table  shows a few best practices identified from the literature that can be followed by the community to provide credible information on social media.

As researchers increasingly turn to SMD to gain insights about disasters, it is necessary to consider a few best practices to be followed so that data can be acquired and analysed efficiently . Drawing from our comprehensive examination of the literature and our own experience in the subject, we offer recommendations to researchers as described in Table .

These recommendations can enhance the effectiveness of data collection and analysis methodologies when working with SMD for disaster management.

Our Social Media Literature Database is available in the form of an Excel file that is open-access . Upon accessing the database, users can employ various functionalities to facilitate their research. The following are a few examples: 1.

Search and Filter. Researchers can search for publications based on specific criteria such as year, keyword, or journal using the search option. Additionally, the filtering option enables users to view publications based on particular conditions (e.g., publications published in a specific year).