Lending ears to unheard voices: An empirical analysis of user-generated content on social media

Abstract

Governments and healthcare organizations increasingly pay attention to social media for handling a disease outbreak. The institutions and organizations need information support to gain insights into the situation and act accordingly. Currently, they primarily rely on ground-level data, collecting which is a long and cumbersome process. Social media data present immense opportunities to use ground data quickly and effectively. Governments and HOs can use these data in launching rapid and speedy remedial actions. Social media data contain rich content in the form of people’s reactions, callsfor- help, and feedback. However, in healthcare operations, the research on social media for providing information support is limited. Our study attempts to fill the gap mentioned above by investigating the relationship between the activity on social media and the quantum of the outbreak and further using content analytics to construct a model for segregating tweets. We use the case example of the COVID-19 outbreak. The pandemic has advantages in contributing to the generalizability of results and facilitating the model’s validation through data from multiple waves. The findings show that social media activity reflects the outbreak situation on the ground. In particular, we find that negative tweets posted by people during a crisis outbreak concur with the quantum of a disease outbreak. Further, we find a positive association between this relationship and increased information sharing through retweets. Building further on this insight, we propose a model using advanced analytical methods to reduce a large amount of unstructured data into four key categories—irrelevant posts, emotional outbursts, distress alarm, and relief measures. The supply-side stakeholders (such as policy makers and humanitarian organizations) could use this information on time and optimize resources and relief packages in the right direction proactively.