Issue |
E3S Web Conf.
Volume 604, 2025
The 4th International Conference on Disaster Management (The 4th ICDM 2024)
|
|
---|---|---|
Article Number | 04006 | |
Number of page(s) | 8 | |
Section | Disaster Monitoring, Broadcasting, Early Warning and Information System | |
DOI | https://doi.org/10.1051/e3sconf/202560404006 | |
Published online | 16 January 2025 |
From tweet to tremor: Enhancing megathrust disaster monitoring and early warning systems in social media
Faculty of Arts and Design, Institut Teknologi Bandung, Ganesha 10 Bandung, Indonesia
* Corresponding author: silviaelsha@gmail.com
This researches how to explores the potential of social media in improving megathrust disaster monitoring and early warning systems, focusing on two regions in Indonesia: Gunung Kidul and Tanggamus. Using a quantitative approach through a questionnaire-based survey, the research reveals that most respondents (70% in Gunung Kidul and 85% in Tanggamus) consider the internet/social media as an important source of information during disasters. Furthermore, 70% of respondents in Gunung Kidul and 85% in Tanggamus are willing to receive official warnings through these platforms. Nonetheless, challenges related to data verification and reliability of information on social media still exist. Only 12% of respondents in Gunung Kidul and 8% in Tanggamus considered social media to be very reliable as a source of disaster information. This finding underscores the importance of collaboration between the government, disaster management agencies and social media platforms to ensure the accuracy of information disseminated. Overall, this study provides empirical evidence on the potential of utilizing social media to improve early warning systems and response to megathrust disasters. The integration of social media into disaster mitigation strategies can provide significant benefits, especially in terms of early detection, enhancing situational awareness, and facilitating communication and collaboration during disasters.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.