The Impact of Misinformation on Social Media in the Context of Natural Disasters: Narrative Review
Bibliographic record
Abstract
Background: Misinformation on social media during natural disasters has become a significant challenge, with the potential to increase public confusion, panic, and distrust. Although individuals rely on social media platforms for timely updates during crises, these platforms also facilitate the rapid spread of unverified and misleading information. Consequently, misinformation can hamper emergency response efforts, misdirect resources, and distort public perception of the disaster's true severity. Objective: This narrative review aims to (1) critically evaluate the available evidence; (2) unpack the dynamics of misinformation on social media in the context of natural disasters, specifically natural hazards, shedding light on the challenges, implications, and potential solutions; and (3) develop a conceptual model linking misinformation, public impact, and disasters, grounded in sourced evidence. Methods: The narrative review examines the impact of social media misinformation in the context of natural disasters. The literature search was conducted using the PubMed database and Google Scholar in April 2024. Studies eligible for inclusion were published in English, with no restrictions on publication date, geographic region, or target population. The inclusion criteria focused on the original research that examined social media misinformation related to natural disasters, specifically natural hazards. Results: From an initial pool of 173 studies, 9 studies met the inclusion criteria for this review. The selected studies revealed consistent patterns in how misinformation spreads during natural disasters, highlighting the role of users, some influencers, and bots in amplified false narratives. The misleading messages disseminated across social media platforms often outpaced official communications, resulting in reduced trust and exacerbating anxiety, stress, and fear among affected populations. This heightened emotional response and erosion of trust in official communications influenced an individual's susceptibility to the misinformation and prompted inappropriate actions. Consequently, such actions led to resource misallocation, overwhelmed emergency services, and diverted attention away from genuine needs. Collectively, these factors negatively impacted public health outcomes and diminished the effectiveness of emergency management efforts, as illustrated in the conceptual model developed to provide a greater understanding of this critical area of study. Conclusions: This narrative review highlights the significant impact of misinformation in the context of natural disasters, specifically natural hazards. It stresses the urgent need for disaster preparedness and response plans that include targeted interventions such as real-time misinformation detection technologies, public education campaigns focused on digital literacy, and proactive debunking initiatives. Implementing these strategies can help mitigate the harmful effects of misinformation, strengthen public trust in official communications, enhance the effectiveness of disaster response, and improve public health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".