COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers

Abstract

Coronavirus disease of 2019 (COVID-19)'s devastating effects on the physical and mental health of the public are unlike previous medical crises, in part because of people's collective access to communication technologies. Unfortunately, a clear understanding of the diffusion of health information on social media is lacking, which has a potentially negative impact on the effectiveness of emergency communication. This study applied social network analysis approaches to examine patterns of #COVID19 information flow on Twitter. A total of 1,404,496 publicly available tweets from 946,940 U.S. users were retrieved and analyzed. Particular attention was paid to the structures of retweet and mention networks and identification of influential users: information sources, disseminators, and brokers. Overall, COVID-19 information was not transmitted efficiently. Findings pointed to the importance of fostering connections between clusters to promote the diffusion in both networks. Lots of localized clusters limited the spread of timely information, causing difficulty in establishing any momentum in shaping urgent public actions. Rather than health and communication professionals, there was dominant involvement of non-professional users responsible for major COVID-19 information generation and dissemination, suggesting a lack of credibility and accuracy in the information. Inadequate influence of health officials and government agencies in brokering information contributed to concerns about the spread of dis/misinformation to the public. Significant differences in the type of influential users existed across roles and across networks. Conceptual and practical implications for emergency communication strategies are discussed.

Understanding Melanoma Talk on Twitter: The Lessons Learned and Missed Opportunities

“…We believe that this is crucial for designing awareness campaigns and interventions. Like our study findings, the content of other health contexts on Twitter, such as vaccination, obesity, and COVID-19, was driven predominantly by medical and scientific experts [ 18 , 22 , 32 ]. However, when information drivers are not from the medical community, the possibility of the spread of misinformation increases; as in case of conspiracy theories pertaining to COVID-19 and wearing masks [ 20 , 21 ].…”

“…To identify the most influential users, top content producers, and disseminators in addition to the main topics and web sources, we conducted SNA using NodeXL [ 27 ] and guided by previous research [ 19 , 22 , 23 ]. To answer RQ1, centrality measure scores were computed: betweenness centrality (BC), in-degree, and out-degree centrality scores [ 27 , 28 ].…”

“…The implementation of SNA techniques in diverse public health topics has grown, mainly when it comes to describing health behavior or information dissemination patterns [ 17 , 18 , 19 ]. It is also noteworthy to mention the role of applying SNA to understand the content landscape and the public perspective during COVID-19 from a societal approach [ 20 , 21 , 22 ].…”

Information sharing practices during the COVID-19 pandemic: A case study about face masks

“…As has been evidenced elsewhere in the literature [ 52 , 96 ], it is the lack of good quality information that drives uncertainty, and as a consequence spawns uninformed, speculative and erroneous content and people will seek information from elsewhere [ 97 ]. One notable example from our dataset of an external resource countering the official government guidance was a domain we categorised as online journalism (which is a blog) ranked 10 th in the ‘most retweeted tweets’ domains categorised as ‘scientific evidence and expertise’.…”

“…Clearly, if they choose to actively engage with the platform, scientists can gain (but this is unlikely to be in every instance) high levels of exposure. However, at the same time, the dominant involvement of non-professional users (in this context referring to those outside the scientific or medical profession) in the dissemination of scientific information, suggests there is potential for these individuals to also have the same level of exposure as media organisations and those considered to be scientific or academic experts [ 97 ]. Previous studies have advised that individuals with potential influence are closely monitored to prevent the spread of misinformation [ 61 ].…”