No abstract

Previous research on information dissemination in emergencies focus on prediction of the volume via abundant models. However, most of these models did not specify different stages of emergencies, and hence making it difficult for public relations (PR) practitioner to make decisions based on needs of each stage in today’s rapid changing media environments. In this study, we introduce the idea of system cybernetics and the method of system identification into information dissemination perspective. Based on the proposed information accumulation probability distribution continuity (IAPDC) model, we provide a quantitative division of the information accumulation process. The durations of each stage and the time points that each stage begins are stated and defined with a quantitative calculation method. Using empirical data from 83 emergencies in 2016 and 2017 covering Weibo, WeChat Platforms and over 20,000 web media, we verify the effectiveness of this method. Next, we use simulation analysis to demonstrate what effects of parameters have on the dissemination process and how do changes on different stages affect the process. Moreover, we also demonstrate the effects of emergencies’ attributes on the information dissemination process and on each stage. Our study complements the gaps in existing communication discipline and provides insight for PR practitioner when dealing with enterprise emergencies.

In the Web 2.0 age, mass media disseminates the disinformation of companies and exerts considerable influence. How to manage this trend in a timely and effective fashion in this big data era has become difficult. In this study, we delve into this issue by trying to identify the core disseminators in the dissemination process. We propose the concept of a disinformation channel and quantitatively analyse these company-related disinformation channels among media outlets. By empirically analysing 4,689 disinformation news values and 330 channels in 2018, we reveal that the disinformation values and negative news values are characteristics. We also build automatic identification models to identify these channels from the media combined with machine learning algorithms. Our study sheds light on disinformation, thus providing managers with an empirical basis upon which to analyse the media and help them address the disinformation problem.

No abstract

No abstract