Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

János Kertész C

49 citations0/0 files storedEPJ Data Science

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Communication and COVID-19 Impact Complex Network Analysis Techniques Complex Systems and Time Series Analysis

Abstract

Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30-70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00263-0.

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References

2020Psychosocial impact of COVID-19 2020Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention 2014The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data 2020Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study 2017Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter 2013Weibo communication and government legitimacy in China: a computer-assisted analysis of Weibo messages on two ‘mass incidents’ †2015Networked framing between source posts and their reposts: an analysis of public opinion on China's microblogs 2020Limited Early Warnings and Public Attention to Coronavirus Disease 2019 in China, January–February, 2020: A Longitudinal Cohort of Randomly Sampled Weibo Users

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