...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers，environment and urban systems >Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters
【24h】

Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

机译：众包数据中的空间偏见：社交媒体内容注意力集中在灾难中的人口群体

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization.
机译：本研究的目的是审查和量化在自然灾害期间的社会渗塑因素，损害索赔和社会媒体关注的关系。社交媒体已成为人们分享和寻求境地了解风险的重要沟通渠道，以应对社区中断，并支持灾害反应。最近灾害信息学的研究已经认识到受灾害影响地区的社交媒体活动的代表性的偏见存在。为了探索这种偏见的相关因素，现有研究已经使用了地理标记的推文来评估灾害影响地区社交媒体活动的程度，以评估弱势群体是否对社交媒体保持沉默。但是，少于1％的推文实际上是地理标记的;因此，试图了解地理贴图推文对一般人群的代表性表明某些人口是过度的或不足的。为了解决这一限制，本研究审查了基于社交媒体内容的位置的关注。该研究进行了基于内容的分析，以筛选与赫维飓风飓风哈维和57个城市在佛罗伦萨飓风飓风中的84个超级社区相关的推文。通过检查社会渗目因素之间的关系，损害索赔的数量以及推文的数量，结果表明，社交媒体关注集中在人口众多，独立于教育，语言，失业和中位数。人口与社交媒体关注的关系的特点是分布式电力法，表明稀疏人口稠密区域之间的变化很大。使用机器学习模型来标记推文的主题，结果表明，社交媒体用户更加关注救援和捐赠相关信息;尽管如此，主题变化跨越具有不同关注程度的区域一致。这些调查结果有助于更好地了解社会媒体的空间集中注意力，关于灾害中的发布和传播情境信息。调查结果可通知紧急经理和公职人员，有效地利用社交媒体数据进行公平资源分配和行动优先级。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号