A Framework for the Interaction of Active Audiences and Influencers on Twitter: The Case of Zika Virus

In this paper, we investigate communication among Twitter users in the context of the 2016 Zika crisis, to understand how influencers and audiences contribute to agenda setting in health crisis communication. We analyse the content of 146,953 Zika-related tweets posted between April and September 2016 and examine how discussion topics vary by network community and user involvement over time. We argue that audiences adopt a broad view of health crisis-related issues and advocate for long-term solutions drawn from theories of active audiences and agenda-setting. Based on our observations on the Zika crisis case, we propose a framework for the dynamics in health crisis communication, which suggests a shift of discourse from a short-term perspective on specific issues to a long-term perspective on broader issues. The research contributes to the KM literature by suggesting a new method for converting individual tacit knowledge to collective explicit knowledge. Applying the framework to the coronavirus pandemic conversations can offer significant insights into the crisis.

Change point detection in social networks using a multivariate exponentially weighted moving average chart

Although the significant role of social networks in communications between individuals has attracted researchers’ attention to the social networks, only few authors investigated social network monitoring in their studies. Most of the existing studies in this context suffer from the following three main drawbacks: (1) using the case-based network attributes such as person experiences and departments instead of the main attributes such as network density and centrality attributes, (2) monitoring the social attributes separately with the assumption that they are independent of each other and (3) ignoring detection of real time of change in the network. To overcome the above-mentioned disadvantages, this research develops a statistical method for monitoring the connections among actors in the social networks with the four most important network attributes consisting of (1) network density, (2) degree centrality, (3) betweenness centrality and (4) closeness centrality. To this end, a multivariate exponentially weighted moving average (MEWMA) control chart is used for simultaneous monitoring of these four correlated attributes. Furthermore, since the control chart usually does not alert a signal in the exact time of change due to type II error, this study presents a change point detection method to reduce cost and time required for diagnosing the control chart signal. Eventually, the efficiency of the proposed approach in comparison with the existing methods is evaluated through a simulation procedure. The results indicate that the suggested method has better performance than the univariate approach in detecting change point.