Scholarlink

<p>Natural disasters are increasingly prevalent worldwide, necessitating the utilisation of diverse datasets for effective disaster response. While geo-social media data represents a valuable resource in this context, the recent restrictions to Twitter data have significantly impacted its availability for disaster research. Alternative social media platforms to Twitter remain underexplored, leading to limited understanding of their potential. To address this gap, we collected posts for a specific use case, Hurricane Ian, from four social media platforms (Mastodon, Reddit, Telegram, and TikTok), and subsequently geoparsed each post. We then computed spatial and temporal patterns and evaluated their correlations to investigate the potential applicability of other data sources for disaster response efforts. While none of the platforms can fully substitute Twitter’s role in disaster management, the findings demonstrated that substantial amounts of potentially valuable data can be sourced from other platforms. Despite consistent overall patterns, subtle differences in temporal activity and spatial distribution suggest that each platform offers unique insights that enhance situational awareness. However, a significant challenge in using these platforms for disaster response is the low spatial accuracy achievable through geoparsing.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

More than just Tweets: the potential of alternative geo-social media data for disaster management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月20日

查看全文
  • Nikola Vračević,
  • Sebastian Schmidt,
  • Merve Keskin,
  • David Hanny,
  • Bernd Resch

摘要

Natural disasters are increasingly prevalent worldwide, necessitating the utilisation of diverse datasets for effective disaster response. While geo-social media data represents a valuable resource in this context, the recent restrictions to Twitter data have significantly impacted its availability for disaster research. Alternative social media platforms to Twitter remain underexplored, leading to limited understanding of their potential. To address this gap, we collected posts for a specific use case, Hurricane Ian, from four social media platforms (Mastodon, Reddit, Telegram, and TikTok), and subsequently geoparsed each post. We then computed spatial and temporal patterns and evaluated their correlations to investigate the potential applicability of other data sources for disaster response efforts. While none of the platforms can fully substitute Twitter’s role in disaster management, the findings demonstrated that substantial amounts of potentially valuable data can be sourced from other platforms. Despite consistent overall patterns, subtle differences in temporal activity and spatial distribution suggest that each platform offers unique insights that enhance situational awareness. However, a significant challenge in using these platforms for disaster response is the low spatial accuracy achievable through geoparsing.