the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jun 2024
Review Article: Leveraging Social Media for Managing Natural Hazard Disasters: A Critical Review of Data Collection Strategies and Actionable Insights
Abstract. This paper critically reviews 250 articles from 2010 to September 2023, analyzing how social media data is utilized in disaster management, addressing challenges in relevance filtering and noise reduction to extract actionable disaster information and enhance decision-making efficiency. The results of our critical analysis are given in a Social Media Literature Database where we categorize each article's information into 7 main categories and 27 subcategories, covering article details, case study regions, disaster events, social media data specifics, data collection and analysis methods, and evaluation methodologies. To assess the effectiveness of social media in providing actionable disaster information, we further classify the articles into 9 categories, covering public discourse analysis, temporal and spatial insights, relevance filtering methods, community/stakeholder collaborations, disaster trends, and resource identification. We also illuminate historical disaster events within the review period and discuss the results through graphical visualizations. Our findings show that natural language processing methods, particularly content analysis, were commonly utilized in the literature, and contribute significantly to basic data filtering by removing noise. Commonly used advanced robust analysis machine learning methods included Support Vector Machines, Naive Bayes, and Neural Networks. We found that proficiency in temporal and spatial analysis of social media data is widespread among the studies, with varying success in implementing effective relevance filtering. Our actionable information categorization revealed a need for further exploration into community interactions and resource identification using social media data during and after disasters. Based on the literature study and our own experience on the subject, we propose six best practices for social media usage in disaster situations for the community and five best practices for researchers to enhance disaster management strategies.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
(3331 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2024-1536', Anonymous Referee #1, 10 Jul 2024
Please see my comments in the attached file
-
AC1: 'Reply on RC1', Lakshmi S Gopal, 31 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1536/egusphere-2024-1536-AC1-supplement.pdf
-
RC2: 'Reply on AC1', Anonymous Referee #1, 05 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1536/egusphere-2024-1536-RC2-supplement.pdf
-
AC2: 'Reply on RC2', Lakshmi S Gopal, 20 Jun 2025
Thank you for your feedback and suggestions.
Citation: https://doi.org/10.5194/egusphere-2024-1536-AC2
-
AC2: 'Reply on RC2', Lakshmi S Gopal, 20 Jun 2025
-
RC2: 'Reply on AC1', Anonymous Referee #1, 05 Aug 2024
-
AC1: 'Reply on RC1', Lakshmi S Gopal, 31 Jul 2024
-
RC3: 'Comment on egusphere-2024-1536', Anonymous Referee #2, 04 Jun 2025
The manuscript presents a critical review of the use of social media data in the context of disaster management, reviewing a total of 250 articles from a range of disciplines. The paper is well-written and offers a valuable contribution to the growing literature on social media in disaster contexts.
The paper would benefit from a clearer focus on its core research questions and a more concise presentation of the findings. The current version includes a significant amount of descriptive content, which could be streamlined to better highlight the most important insights.
Major Comments:
1. Length and focus of the study: The manuscript is quite long (44 pages, including 19 figures and 4 tables). While comprehensive, much of the content is descriptive and does not directly address the core research questions. I recommend moving some of the less critical descriptive sections (particularly in Sections 3.3, 4.2, and 4.4) to the appendix. For me, the most insightful sections were 4.3 and the discussion in Section 5. A focus on these could improve the impact of the paper.
2. Clarification of research questions and key insights: The paper could more clearly articulate its central research questions and ensure that the findings directly address them. In particular, I found the second research question on the “actionable information” derived from social media more relevant and of interest to a broader audience. In contrast, the first question on “exclusion criteria in relevance filtering” is more technical and may be of limited interest to non-specialist readers.
3. Practical examples in the introduction: The introduction would benefit from one or two concrete examples illustrating the kind of information that can be derived from social media in disaster contexts and how these tools have been used by researchers. This would help orient readers who are less familiar with the data sources or their potential applications.
4. Keyword selection: The search strategy seems to have focused on Twitter, with no mention of other major platforms such as Facebook, Instagram, or Weibo. This is particularly surprising given that some of these platforms have significantly higher user bases and broader geographic reach. They have also been extensively used in disaster contexts (e.g., Facebook). The rationale for this focus should be clearly explained, and the implications of this potential bias should be acknowledged more explicitly in both the methodology and the interpretation of findings (e.g., when the authors find that the great majority of studies in their database uses Twitter as noted in line 338 and elsewhere).
5. Inclusion of studies not using social media: Section 3.3.4. describes the data used in studies distinguishing whether article have utilized social media data or not. It is unclear why studies not using social media were included in the database, given the paper’s stated focus on social media. Of course, the authors could use these other articles as reference points to show the advantages or disadvantages of the use of social media, but this does not really happen in the analysis. Instead, when describing their database, the authors also refer to those articles not using social media data, which may confound the analysis.
6. Stronger emphasis on key messages: While the paper shows great technical detail, the manuscript would benefit from a more focused discussion of key takeaways. Specifically, what are the major advantages, disadvantages, and challenges of using social media data in disaster contexts? These insights could be more prominently featured in the abstract, conclusion, and discussion sections (see also my comment 2 on the research question above).
Minor Comments:
7. Line 35: The sentence “Existing literature reviews on social media data (SMD) platform evaluations, data collection tools, and analysis methods over time” is missing a verb.
8. Line 176: Typo in “The may however be other keywords...”
9. Line 180ff: The authors note that relevant studies may have been missed. Would it not have been advisable to revise the keyword strategy to more comprehensively capture the literature?
10. Line 288: Missing word: “Such data is considered credible when compared to the data extracted from social media platforms as it may include false information.”
11. Line 557: The phrase “affected more than 20 lakh people” may be unclear to international readers. Maybe better to replace with “2 million people”
12. Section titles: Some subsection titles could be made more meaningful and specific. For example, “4.2. Early Works” could clarify the time period; “4.2.2. Previous Works on Social Media Analytics” could clarify what is meant with “previous”, etc.
Citation: https://doi.org/10.5194/egusphere-2024-1536-RC3 -
AC3: 'Reply on RC3', Lakshmi S Gopal, 20 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1536/egusphere-2024-1536-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Lakshmi S Gopal, 20 Jun 2025
Data sets
Social Media Literature Database Lakshmi S. Gopal, Rekha Prabha, Hemalatha Thirugnanam, Maneesha Vinodini Ramesh, and Bruce D. Malamud https://doi.org/10.5281/zenodo.10803017
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 862 | 529 | 86 | 1,477 | 36 | 58 |
- HTML: 862
- PDF: 529
- XML: 86
- Total: 1,477
- BibTeX: 36
- EndNote: 58
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
