How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

BACKGROUND It has been argued that information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. For example, health information could be transmitted from one to many (i.e. broadcasting), which is similar to how traditional mass media passes information to the general public. Health information could also be transmitted from many to many (i.e. viral spreading), which is analogous to the spread of infectious diseases. OBJECTIVE The aim of this study is to determine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. On Twitter, influential users are those whose tweets receive a large number of retweets. METHODS Our data was purchased from GNIP, the official Twitter data provider. We obtained all Ebola-related tweets (including retweets and replies) posted from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships (who follows whom on Twitter). Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Disseminators received fewer retweets than expected based on their number of followers, common users and influential users received as many or fewer retweets than expected, and hidden influential users received more retweets than expected. RESULTS On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcast model was more pervasive than viral spreading. Furthermore, we found that influential users and hidden influential users can trigger more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. CONCLUSIONS The broadcast model was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger a lot of retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion, because the hidden influential users can receive more retweets than expected based on their limited number of followers. However, challenges remain due to uncertain credibility of these hidden influential users.