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Social Signals: Harnessing Social Media Data for Disaster Management

EasyChair Preprint no. 10973

18 pagesDate: September 26, 2023


This study investigates the collection of social media signals without exclusive reliance on application programming interfaces (APIs). The current research on collecting social signals for disaster management is primarily focused on APIs. This approach is valuable, but it also presents a range of challenges that need to be taken into account. Thus, the extent and regularity of data collection may be impacted, posing challenges in obtaining comprehensive and up-to-date information. In light of this knowledge gap, we put forth a compelling argument in support of non-API approaches for gathering social signals from social media platforms. To answer the research questions, a qualitative methodology employing an inductive approach was used to gather and analyze data from officers working in disaster management organizations (DMOs). By adopting this approach, noteworthy themes and patterns emerged and were carefully examined, ultimately resulting in the derivation of the research findings. The study highlights the potentials of social signals in enhancing decision-making across various phases of disaster management. Through innovative techniques, DMOs can leverage social signals from public posts, comments, and interactions to gain in-sights into user sentiments, opinions, and real-time updates. These insights greatly assist decision-making at different stages of disaster management, including preparedness, response, recovery, and mitigation. Overall, the study emphasizes the effectiveness of gathering social signals from social media platforms without relying solely on APIs, highlighting their potential to improve decision-making in disaster management.

Keyphrases: crisis response, Crowd Sensing, data collection, Data-driven Disaster Management, Disaster Management, Disaster Phases, Inductive Coding, Inductive Coding., Public Sentiment Analysis, Social Media Analytics, social media data, Social Signals

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Safianu Omar and Jean-Paul Van Belle},
  title = {Social Signals: Harnessing Social Media Data for Disaster Management},
  howpublished = {EasyChair Preprint no. 10973},

  year = {EasyChair, 2023}}
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