Inference models vary in complexity and required customization. For example, a simple descriptive study may only require correlations (e.g. Pearson correlation) or the use of a traditional nonparametric model (e.g. multivariate regression). Or perhaps the study is an experiment attempting to understand a causal relationship that requires the use of a more customized graphical model (e.g. Markov Random Fields). In all cases, we need to assess the soundness of the selected model or determine if an appropriate model (algorithm) does not exist and a new one needs to be developed. In some cases, we also need to train the model using labeled data and tune the model parameters. This is a process that is well understood in computer science. Unfortunately, social media data have new forms of uncertainty – including non-random noise, partial information, and misinformation – that are not well understood. While there is a growing literature about the increasing impact of misinformation and the importance of finding ways to correct it, we still do not understand how randomly these new forms of uncertainty are distributed or their impact on the construction of different models, particularly longitudinal ones. There are also biases that are specific to different social media portals, e.g. times of day people post, types of posts that are common, etc. These need to be considered during model construction.