Identifying underlying influential factors in information diffusion process on social media platform: A hybrid approach of data mining and time series regression

As the prevailing online communications paradigm, social media platforms are considered to be the fastest medium for sharing and diffusing information. But what influences the spread of information through these platforms? The content of the post? The sentiments contained? Or the characteristics of user's behavior? To explore which factors promote the spread of information through social media, we developed a data analytics method that combines data mining with time series regression. We then applied this analytical framework to the L group Double 11 false advertising scandal, which blew up on the Sina microblog – a public hot trend that attracted the attention of millions of people. Our analysis reveals how three factors – user activity, emotional changes, and public attention – interact and the role they play in the spread of information. Among these factors, sentiment polarity and reposting are found to be the two main drivers of information diffusion. Emotional contagion accelerates the spread of information when the event first breaks (known as the accumulation period), while reposting does more to spread information once the event has gained some traction (the diffusion period). Surprisingly, the topic of public concentration in the event has a significant impact on the spread of the event in the accumulation period, but the effect shades away during the diffusion and convergence periods, i.e., the farther relations among topics are tied, the less public interest is abating on the event – a finding that is supported by cognitive load theory. However, although public attention shows little influence in the diffusion process, it does reveal how consumers shift their attention to different subtopics over time. Overall, our analysis sheds some light on how online events evolve and 'go viral'. Notably, this study not only explores how underlying factors dynamically influence the information diffusion process, but also offers insights into how to manage information diffusion processes in practice.