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

Since the first case of Coronavirus Disease of 2019 (COVID-19) was confirmed in the United States on January 21, 2020, over 13 million people in the U.S. have confirmed cases of COVID-19. Despite multiple national- and state-wide interventions and prevention measures including banning non-essential travel and stay-at-home orders, on April 12, the USA became the nation with the most deaths globally. As of December 2, the U.S. death toll surpassed 271,000.

Providing up-to-date, accurate information, delivering key messages timely to the public, and controlling the spread of dis/misinformation can play a crucial role in managing epidemics (Homeland Security Council (US), 2006; World Health Organization, 2009; Centers for Disease Control and Prevention, 2014; Department of Homeland Security, 2018). 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. COVID-19 is the first pandemic of its kind in the age of social media. The amount and nature of information available to the public has changed significantly and is constantly evolving. Unfortunately, a crucial but surprisingly understudied phenomenon is the diffusion of health information on social media (Zhou et al., 2018; Aramburu et al., 2020).

Twitter, a microblogging service, has become one of the most important sources of realtime news updates, with more than 64 million users in the U.S. (Kemp, 2020). According to a recently published Pew Research Center report, 68% of American adults get news on social media and 71% of Twitter users responded they use it to get daily news (Matsa and Shearer, 2018).

Twitter users send and receive short posts called tweets about any topic. Tweets can be up to 280 characters long and can include user mentions and keywords. Users can forward other users’ tweets and these forwarded messages are called retweets. Mentions can be used with the at symbol “@” before a username to identify a specific user. By retweeting or mentioning, users are interacting with other users and share information in a conversation-like manner (Wang et al., 2015). The hashtag symbol “#” can be used before a relevant keyword to initiate conversations or contribute to discussions of existing topics by showing their tweets in Twitter search. The use of the hashtag on Twitter indicates self-association of a user with an issue (Gruzd et al., 2011; Gleason, 2013).

As users interact in Twitter space, they form connections that emerge into complex social network structures. Essentially, the connections are asymmetric, since a user who is retweeted or mentioned by another user does not necessarily have to reciprocate by retweeting or mentioning them back. Due to this asymmetry, users can re-create and reinforce traditional hierarchical network structures in Twitter by relying on just a few information sources or by choosing to limit interactions to a select group of similar others (Himelboim et al., 2017). Thus, the connections built among users are indicators of information sharing and network structures reflect patterns of information flow (Himelboim et al., 2017; Majmundar et al., 2018).

There are many studies that have examined the structure of communication networks on Twitter that provide insights about information flow during political campaigns and social movements (Himelboim et al., 2012; Ansari, 2013; Harris et al., 2014; Kruikemeier, 2014; Shin et al., 2017, 2018; Recuero et al., 2019). The patterns of communication and influential groups can vary across topics, cultures, or languages. Although few recent studies investigated Ebola information dissemination patterns (Harris et al., 2018; Liang et al., 2019), their analysis was limited to retweet network, which together with mention network can provide an understanding of information flow on Twitter (Conover et al., 2011). To the best of our knowledge, this is the first study to examine both the retweet and mention networks to understand the diffusion of health information on Twitter among Americans during a pandemic.

Structural characteristics were examined at the network level to address our overarching research question: Is the current Twitter’s COVID-19 communication network effectively leveraged to facilitate the flow of valid information during this crisis? Information can diffuse most effectively during crises if the network is sufficiently dense with low rates of clustering (Himelboim et al., 2017). Ideally, the Twitter COVID-19 communication network would have large audience and spread information quickly. Thus, we evaluated information flow in the retweet and mention networks with particular attention paid to its connectivity, modularity and direction of information flow.

Influential COVID-19 Twitter users were identified as information sources, disseminators and brokers. Ideally, COVID-19 information sources would be medical/health professionals to emphasize credibility, disseminators would be communication/journalism professionals to maximize reach, and brokers would be public health and government officials to ensure that information is accurate and continues to flow (World Health Organization, 2009; Centers for Disease Control and Prevention, 2014). Thus, the aim of this study was to determine the characteristics of COVID-19 Twitter users by comparing the professional categorizations by their roles as sources, disseminators, or brokers in the retweet and mention networks. We hope this study will significantly contribute to public health by helping devise more effective emergency communication strategies and ultimately help mitigate the spread of disease and reduce misinformation.

Despite Twitter’s reputation as an effective medium to connect people and facilitate public communication, the topic of COVID-19 did not bring its users together. Both the retweet and mention networks were sparsely connected, exhibiting a large number of small distinct clusters. A study from Kaur and Singh (2016) reported that disconnected networks often result from distrust in information sources. Consistent with their finding, more than half of the COVID-19 information was generated by non-professional users, increasing the likelihood of encountering false information and thereby potentially spreading misinformation.

Moreover, dominant involvement of non-professional users was observed in the information dissemination process. In both the retweet and mention networks, communication professionals were only marginally involved and there were almost no health professionals among the disseminators. Since publicly shared information has a direct impact on the development of public behaviors, it is very important to consider the type of people who act as information disseminators during medical crises (Hilton and Hunt, 2011; Staniland and Smith, 2013). Findings by Keshvari et al. (2018) warned about biased and misleading content that ordinary people, who are not trained to objectively perceive risks and benefits, disseminate with personal speculations and interpretations during epidemics. Communication professionals, on the other hand, are trained to investigate all possible aspects and implications of information before promoting the information. In this process, communication professionals are often dependent on health professionals to substantiate facts and provide balance by ensuring pluralistic aspects and implications of the pandemic (Ahlmén-Laiho et al., 2014). Increasing willingness on the part of communication professionals to disseminate accurate information and to cooperate with health professionals, may be critical to control the spread of dis/misinformation and prevent public confusion.

In both the retweet and mention networks, information flow was highly concentrated within a core cluster, comprised of a few influential users and their own clusters; information flow to the rest of the network (the other clusters) was severely restricted due to the limited connectivity. This suggests that the networks facilitate the diffusion of COVID-19 information if brokers integrate with their communities and clusters. In the context of social media communications, the limited connectivity between clusters means that networks would break into isolated components, separated by redundant and unnecessary information and that information will, more often than not, be trapped within its own cluster. Brokers, on the other hand, create paths for information diffusion and make global information flow easier to attain, if and when they are activated (González-Bailón and Wang, 2016). In a pandemic, a balanced approach to centralized control and management of information is critical in helping public audiences understand the threat and what actions should be taken (Homeland Security Council (US), 2006; Department of Homeland Security, 2018). A proper course of action as information brokers must be taken by government officials to be complete, valid and reliable (Homeland Security Council (US), 2006; Department of Homeland Security, 2018). Unfortunately, however, that was not happening in the current Twitter’s COVID-19 communication network. Neither the retweet network nor the mention network showed enough influence of government officials as information brokers; in both networks, information flow was primarily maintained by health professionals. Developing social media communication guidelines for officials and national agencies that offer a starting point to foster connections and training to control or promote information flow may help ensure effective information flow and make necessary information timely and accessible to those who need it in the process of emergency response.

Both the retweet and mention networks exhibited a scale-free hierarchical structure, with unidirectional information flow. Due to preferential attachment a small number of influential users get, such network structures can be much more effective at rapid information diffusion for timely response and national solidarity during crises (Himelboim et al., 2017; De Brún and McAuliffe, 2018); because a small number of influential users can command a large and disproportionate number of other users and those users then will affect all the other users in their local network, a whole subsystem can be covered in just a few steps, making it relatively easier to keep everyone informed of relevant information such as risks and action items. At the same time, however, such network structures can also be vulnerable to false information and its diffusion can be easily distorted by just one or a few influential users’ absence in the network (Lossio-Ventura and Alatrista-Salas, 2017; De Brún and McAuliffe, 2018); for instance, if one or two influential users were removed or left the network, it would leave a major gap in support for most users thus interrupting information flow; similarly, a single piece of misinformation can be a risk factor for the entire system because of the fast nature of information dissemination. Monitoring information flow and ensuring that the public can rely on a consistently valid source of information via controlled channels at all stages of a pandemic communication planning may help the emergency communication network be more resilient and stable.

The visualization results suggested that influential users in the retweet and mention networks may have different reasons to engage in COVID-19 communication. Different interaction patterns and preferences in interaction form in Twitter networks have been previously shown to result in part from differences in the type of messages, which may reflect the reasons users engage in the communication (Conover et al., 2011; Himelboim et al., 2017). Conover et al. (2011) found that, in Twitter’s political discourse where the retweet network was highly polarized while the mention network was not, users tended to retweet other users whom they agreed with politically, while they interacted with users whom they disagreed with more frequently using mentions to argue or share their views. COVID-19’s retweet and mention networks did not exhibit the same connectivity among users. Interacting closely, influential users in the retweet network shared information with each other, and the interactions among influential users facilitated less influential users’ access to information by connecting each of their clusters in the network. In contrast, the absence of interactions among influential users in the mention network led to the more limited information flow across clusters. Studies are needed to investigate whether and how differences in information flow tendencies in health communication represent differences as a function of information type.

This study has some noteworthy limitations. Data collection was restricted to English messages, which may limit generalizability to other languages. The study was unable to access private networks – only publicly available tweets were retrieved for the analyses. Although a majority of Twitter users (87%) reported they keep their accounts public (Wojcik and Hughes, 2019), the findings may not reflect the characteristics and attitudes of private users. Many additional aspects of information diffusion regarding the topic of COVID-19 were not captured by the indicators of information sharing – retweets and mentions. For example, the current study did not include followers-followees structure since it has been reported that influential users are those who have an active audience who mentions or retweets the users, instead of the large number of followers (Cha et al., 2010) and the number of followers/followees does not fully explain users’ actual activities (Hamzehei et al., 2017); however, it may be possible that the structure explains other aspects such as the impact of the information shared. The current version of the Twitter API does not store users who retweet retweeters. A prior study on information spread on the retweet network connection identified that most (91%) of retweets are directly retweeted from the initial message (Liang et al., 2019). However, the unavailability of the full content record may prevent us from further knowing the pattern of information diffusion among intermediate retweeters. There are no comparable analyses to determine cut-off values of network indices to be high or low. Thus, our only basis was our own interpretation of the data. Social networks are often only weakly scale-free even in cases where the power-law distribution is observed (Broido and Clauset, 2019). Future research should investigate the robustness of the scale-free structures and interpretability of power-law distribution. Drawing inferences solely based on a visual inspection requires further statistical confirmation.

This study examined the COVID-19 communication network on Twitter to provide insights about health information flow among Americans during a pandemic. Structural characteristics of retweet and mention networks were quantified and described with different metrics (size, density, connectivity, modularity). Influential users (information sources, disseminators, brokers) in each network were identified and the nature of the influential users were conceptually assessed. Results showed that in both retweet and mention networks, the topic of COVID-19 created large fragmented Twitter populations into multiple communication channels, each with its own audience and information sources. The study also found the absence of reliable sources, disseminators that can provide timely, accurate information, and proper management of information flow. These results have implications for understanding and predicting information diffusion in urgent public health communication. Overall, the findings emphasized the importance of connecting users to the essential resources and distinguishing credible information among a huge amount of information being shared. As social media becomes a more heavily used news source, the effectiveness of crisis management depends more on the type of information shared among its users and the user reachability in the network. Our work opens several new questions about the underlying structures of social media communication network. Future studies may expand this research, exploring how user clusters are formed and examining how relationships between information type and degree of influence differ by cluster or change over time.