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Record W4413161366 · doi:10.1016/j.procs.2025.07.061

Information Seeking and Sharing as Gratifications Explaining Mobile Social Media Use in Pre-, During and Post-Disaster Management

2025· article· en· W4413161366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceSocial mediaUses and gratifications theoryInformation sharingMobile deviceInternet privacyMobile mediaWorld Wide WebMultimedia

Abstract

fetched live from OpenAlex

This paper suggests a model and empirical study to explore the acceptance of mobile social media and other applications for disaster risk mitigation in sub-Saharan Africa, using gratification and technology acceptance theories. It identifies information seeking and sharing as gratifications leading to increased intention and actual usage of mobile platforms for disaster management. Overall, our model suggests that social media and app usage positively impact disaster mitigation, preparedness, awareness, response, coping, and adaptation. Emphasizing gratification theory, particularly in marginalized African communities, and confirming the relevance of technology acceptance theories, our study promises to contribute to IT-enabled disaster management literature. Data will be collected through a survey from different regions across Sub-Saharan Africa, with subsequent analysis using partial least squares structural equation modelling (PLS-SEM).

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.005
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.251
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it