Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and COVID-19
Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and COVID-19
复制标题
DOI：
10.1177/03611981231168120
复制
发表时间：
2022-10
影响因子：
1.7
通讯作者：
Khondhaker Al Momin;Imran Kays;Ph.D Arif Mohaimin Sadri
中科院分区：
文献类型：
--
作者：
Khondhaker Al Momin;Imran Kays;Ph.D Arif Mohaimin Sadri
在线社交网络使不同的机构和公众能够在重大灾害期间进行互动并分享潜在的风险和保护行动。这项研究揭示了在飓风劳拉和COVID-19大流行期间的危机沟通模式。飓风劳拉是有记录以来在美国路易斯安那州卡梅隆登陆的最强（4级）飓风之一。使用应用程序编程接口（API），这项研究利用了通过最近发布的学术跟踪从Twitter获得的大规模社交媒体数据，该数据提供了完整和无偏见的观察结果。这些数据捕获了来自受飓风劳拉威胁的脆弱地区的活跃Twitter用户共享的公开推文。在线社交网络基于Twitter的用户影响力特征（即，提及或标记），其允许在发布推文时通知其他用户。利用网络科学理论和先进的社区检测算法，该研究将这些网络分为21个不同大小的组件，其中最大的组件包含8个定义明确的社区。几种自然语言处理技术（即，词云、二元语法、主题建模）被应用于这些社区中的用户共享的推文，以观察他们在重大复合危机期间的冒险或规避风险的行为。当地新闻媒体、广播、大学和流行体育页面的社交媒体账户与当地居民密切互动。相比之下，该地区的应急管理和规划单位与公众接触较少。这项研究的结果为设计有效的社交媒体沟通指南提供了新的见解，以便在未来的灾难中更好地应对。
Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during Hurricane Laura compounded by the COVID-19 pandemic. Hurricane Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana, U.S. Using an application programming interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Hurricane Laura. Online social networks were based on Twitter’s user influence feature (i.e., mentions or tags) that allows notification of other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into 21 components of various size, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those which heavily involved and closely interacted with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters.