Measuring the Impact of Early Stage Characteristics on Long Term Success of Online Communities
The proliferation of online communities has created exciting opportunities to study the mechanisms that explain the success of online groups. While a growing body of research investigates community success through a single measure — typically, the number of participants — we argue that there are multiple ways of measuring success. Furthermore, we argue that the early stage dynamics within a newly created community can have a long term impact in the success of a community, but which dynamics impact success depends on the type of success. In this project we aim to take a multi-method approach to investigate the causal impact of several early-stage attributes of online communities such as user engagement, linguistic style, network structure, and user composition on the success of the communities. Based on prior work, we will characterise success using four metrics: (i) growth, (ii) user retention, (iii) long term survival, and (iv) volume of content production.
Our approach will involve two steps: First, drawing on a large Reddit data that is currently available, we will apply quasi-causal inference techniques such as propensity score matching in order to quantify the causal impact of several community attributes on success. Based on these findings, we will develop possible interventions or recommendation that can have a positive impact on the success of online communities. Second, we will partner up with social media platforms such as Reddit, Voat, Digg, Campus Society, Stacksity, or Snapzu to conduct experiments that test the performance and efficiency of our interventions. The experiments will involve applying customized interventions to newly created communities that will optimize their success relative to goals of the community.