Suchergebnisse

People all over the world were under severe stress and were concerned about their health after a devastating pandemic struck the world in the form of a novel coronavirus disease (COVID-19) in late December 2019. Many nations imposed strict lockdowns and quarantines, causing citizens to maintain social isolation, throwing many companies to a halt. Thousands of people took to Twitter during these challenging circumstances to express their feelings about being caught in the middle of a storm. Twitter witnessed an outpouring of emotions ranging from fear, anger, and sadness associated with the spread of a novel virus that has no known cure, to voices of support and trust for nations' official response to the pandemic. In studying the emotional response (anger, fear, and sadness) on Twitter about the COVID-19 crisis, we thus see a tale of two crises unfold—choosing health or economy. We capture collective emotions on social media and investigate the patterns and impact of these negative emotions during various stages of the disease outbreak. It also provides crucial insights to health officials and government agencies on communicating crisis information to the public via social media.

AbstractDuring emergency situations, a quick and concise summary from the deluge of messages improves "situation awareness" and enables informed decisions. However, this is challenging due to the volume, variety, and veracity of information. Grounded in the Situation Awareness Theory, this study presents a streamlined protocol to process millions of social media messages and render them in an easily comprehensible format for various stakeholders to gain deeper insights. Specifically, using a design science approach, we develop a set of artifacts using incremental hierarchical clustering and enhanced text summarization algorithm to produce informative summaries under crisis situations. We implemented our protocol on 2.5 and 3 million tweets collected during the two major hurricanes in 2017 and 2018. The results show that our protocol can derive critical information not captured by Twitter's search tools and mainstream news media and significantly improve on accuracy and efficiency when compared to other contemporary tools.