  In theory, a major advantage to the big data approach in studying online
communities is that it should be possible to collect a representative random
sample from a broadly defined population. However, in practice, data collection
processes are not formalized, even for famous social media platforms such as
Twitter and Facebook. As a result, there is ambiguity left on questions such as
"how much data is enough?" and how representative are the samples of the
broader population being studied in online social networks. In this paper, I
propose a focused back-and-forth crawl approach and a validated seed choice
method for collecting network-level data from Twitter. The proposed crawl
method can extract community structures without needing a complete network
graph for the Twitter network and validate its size using "reference score". It
also takes care of the sampling size problem in Twitter by tracking the
percentage of known nodes that have been included in the data. Thus, solving
most major problems in Twitter data collection procedures and moving a step
further to formalizing data collection methods for the platform. Once the
communities are crawled, and the network graph is clean and complete; it is
then possible to train Machine Learning classifiers using communities as
features to predict political affiliations of users on a larger scale. As a
case, I used the proposed method for separating French political communities on
Twitter from the global Twitter community and knowing the political
affiliations of users on a continuous scale.
