Jack Lipei Tang | Communication Scholar

Severe public crisis such COVID-19 pandemic entail coordinated communication between politicians and public health agencies. The study explores how and why U.S. politicians share messages from health agencies on social media during COVID. Proposing a multi-theoretical, multi-level (MTML) framework to understand the phenomenon, we draw upon the Advocacy Coalition Framework and Crisis and Emergency Risk Communication theory and conceptualize politicians’ public health communication as serving the dual functions of policy and risk communication. With bipartite longitudinal network modeling, our analysis finds a fragmented national message-sharing network deprived of central federal leadership and clustered around state-level actors such as local health agencies and state governors. The politicians’ party affiliation and positions on COVID-19 policies significantly impacted whether they would help distribute messages from public health agencies. Health agencies’ message features such as expression of certainty and use of analytical words also influenced politicians’ message sharing patterns. These findings suggest the pandemic communication is both a policy advocacy and a risk and crisis communication process. This integrated theoretical approach offers explanations of information sharing dynamics between politicians and health agencies, two major information sources for a wider public.

Social media influencers (SMIs) have prevailed in online communication networks and can play key roles in connective action. However, how influencers connectively drive activism communication network is less clear. This study conceptualizes issue influencers as users who emerge as influential nodes in shaping the communication network in a contentious political issue niche. A new typology of influencers is proposed based on both issue and platform influence. Drawing on connective action theory and SMI studies, this study employed a Stochastic Actor-Oriented Model to examine how structural (endogenous) processes and influencer-level (exogenous) characteristics explain the communication network dynamics among issue influencers based on 200,000 Weibo posts from approximately 150,000 unique users about a gender policy discussion in China. The results show reciprocity, closure triplets, and influencer-level attributes including issue stance and influencer status significantly drive the longitudinal activism communication network. This study suggests that, when political influencers are absent, non-political influencers and regular users can effectively lead a contentious communication network on a platform with increasing censorship and suppression.

Networked social influence and strategic information manipulation are two social mechanisms fueling misinformation spread in online communities. However, it is unclear how these two mechanisms differ in their impacts. We conducted social network analyses on two online communities sharing misinformation concerning refugees in 2016 and COVID-19 in 2020. The results robustly showed that online misinformation spread is transitive and positively associated with members’ embedded authority (i.e., the extent to which members’ information is exclusively shared within the focal community). At the same time, strategic misinformation sharing by members of high community loyalty (i.e., targeted information sharing within the community) is less likely to gain momentum. The impact of bots on misinformation is contingent. Findings suggest that networked social influence is a more powerful driver of misinformation spread than strategic information manipulation.