Computational Story Lab, Vermont Complex Systems Center, The Vermont Advanced Computing Core, Department of Mathematics & Statistics, The University of Vermont, Burlington, VT, United States of America.
Department of Nutrition and Food Sciences, The University of Vermont, Burlington, VT, United States of America.
PLoS One. 2021 Jun 9;16(6):e0251704. doi: 10.1371/journal.pone.0251704. eCollection 2021.
In September 2017, Hurricane Maria made landfall across the Caribbean region as a category 4 storm. In the aftermath, many residents of Puerto Rico were without power or clean running water for nearly a year. Using both English and Spanish tweets from September 16 to October 15 2017, we investigate discussion of Maria both on and off the island, constructing a proxy for the temporal network of communication between victims of the hurricane and others. We use information theoretic tools to compare the lexical divergence of different subgroups within the network. Lastly, we quantify temporal changes in user prominence throughout the event. We find at the global level that Spanish tweets more often contained messages of hope and a focus on those helping. At the local level, we find that information propagating among Puerto Ricans most often originated from sources local to the island, such as journalists and politicians. Critically, content from these accounts overshadows content from celebrities, global news networks, and the like for the large majority of the time period studied. Our findings reveal insight into ways social media campaigns could be deployed to disseminate relief information during similar events in the future.
2017 年 9 月，飓风“玛丽亚”以 4 级风暴强度袭击了加勒比海地区。飓风过后，波多黎各的许多居民将近一年没有电或干净的自来水。我们使用 2017 年 9 月 16 日至 10 月 15 日期间的英文和西班牙文推文，研究了岛内和岛外对“玛丽亚”飓风的讨论，构建了飓风灾民与其他人之间的通信时间网络的代理。我们使用信息论工具来比较网络中不同子组的词汇发散度。最后，我们量化了整个事件中用户突出度的时间变化。我们在全球范围内发现，西班牙语推文更经常包含希望和关注帮助者的信息。在本地层面，我们发现，在波多黎各传播的信息大多来自该岛本地的来源，如记者和政治家。至关重要的是，在研究的大部分时间里，这些账户的内容掩盖了名人、全球新闻网络等的内容。我们的研究结果揭示了社交媒体活动在未来类似事件中传播救援信息的方式。
