Measuring Contagion on Social Media

Abstract

For more than a decade social media have connected people virtually, thereby creating digital social networks with different purposes and affordances. People typically co‐orient, leading to some content going viral and becoming “contagious” for other network members. Importantly, the complex contagion phenomenon can now be captured more easily using digital tools that became part of the method arsenal in computational social science. Conceptually, social contagion describes a nonpermanent event that, when it occurs, develops over time with specific social network dynamics. It can be assessed using both observational and controlled study designs that, importantly, also come with novel ethical challenges that need to be considered. Identifying social media influencers using centrality measures can be the starting point for measuring social media contagion, thus understanding the social contagion dynamics as “rippling effects” following a “three degrees of influence rule” within social networks of connected members, friends, and friends of friends. Over time, the contagion establishes itself as part of temporal clustering and as a result of a human tendency for assortative mixing and homophilic preferences. In combination with centrality measures and indicators for network homogeneity, the lifetime of social media content is a good measure to further describe not only the dynamics behind the adoption of viral content on social media, but also the emergence of echo chambers.