Abstract 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.

中文翻译：

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众包数据中的空间偏差：社交媒体内容注意力集中在灾害中的人口稠密地区

摘要 本研究的目的是检查和量化自然灾害期间社会人口因素、损害索赔和社交媒体对地区的关注之间的关系。社交媒体已成为人们分享和寻求情境信息以了解风险、应对社区破坏和支持灾害应对的重要沟通渠道。最近对灾害信息学的研究已经认识到，在受灾害影响的地区，社交媒体活动的表现存在偏见。为了探索这种偏见的相关因素，现有研究使用带有地理标记的推文来评估受灾地区社交媒体活动的程度，以评估弱势群体是否在社交媒体上保持沉默。然而，只有不到 1% 的推文实际上带有地理标记；所以，试图了解带有地理标签的推文对一般人群的代表性已经表明某些人群的代表性过高或过低。为了解决这一限制，本研究检查了基于社交媒体内容对位置的关注。该研究进行了基于内容的分析，以过滤与飓风哈维期间休斯顿的 84 个超级社区和佛罗伦萨飓风期间北卡罗来纳州的 57 个城市相关的推文。通过检查社会人口因素、损害索赔数量和推文数量之间的关系，结果表明社交媒体的注意力集中在人口稠密的地区，与教育、语言、失业和收入中位数无关。人口与社交媒体关注度之间的关系以次线性幂律为特征，表明人口稀少地区之间存在很大差异。使用机器学习模型标记推文的主题，结果表明社交媒体用户更关注救援和捐赠相关信息；尽管如此，不同关注程度的领域之间的主题差异是一致的。这些发现有助于更好地了解社交媒体关注在发布和传播灾害情况信息方面的空间集中度。调查结果可以告知应急管理人员和公职人员有效地使用社交媒体数据来公平分配资源和确定行动优先级。结果表明，社交媒体用户更关注救援和捐赠相关信息；尽管如此，不同关注程度的领域之间的主题差异是一致的。这些发现有助于更好地了解社交媒体关注在发布和传播灾害情况信息方面的空间集中度。调查结果可以告知应急管理人员和公职人员有效地使用社交媒体数据来公平分配资源和确定行动优先级。结果表明，社交媒体用户更关注救援和捐赠相关信息；尽管如此，不同关注程度的领域之间的主题差异是一致的。这些发现有助于更好地了解社交媒体关注在发布和传播灾害情况信息方面的空间集中度。调查结果可以告知应急管理人员和公职人员有效地使用社交媒体数据来公平分配资源和确定行动优先级。