  Qualitative research provides methodological guidelines for observing and
studying communities and cultures on online social media platforms. However,
such methods demand considerable manual effort from researchers and can be
overly focused and narrowed to certain online groups. This work proposes a
complete solution to accelerate the qualitative analysis of problematic online
speech, focusing on opinions emerging from online communities by leveraging
machine learning algorithms. First, we employ qualitative methods of deep
observation for understanding problematic online speech. This initial
qualitative study constructs an ontology of problematic speech, which contains
social media postings annotated with their underlying opinions. The qualitative
study dynamically constructs the set of opinions, simultaneous with labeling
the postings. Next, we use keywords to collect a large dataset from three
online social media platforms (Facebook, Twitter, and Youtube). Finally, we
introduce an iterative data exploration procedure to augment the dataset. It
alternates between a data sampler -- which balances exploration and
exploitation of unlabeled data -- the automatic labeling of the sampled data,
the manual inspection by the qualitative mapping team, and, finally, the
retraining of the automatic opinion classifiers. We present both qualitative
and quantitative results. First, we show that our human-in-the-loop method
successfully augments the initial qualitatively labeled and narrowly focused
dataset and constructs a more encompassing dataset. Next, we present detailed
case studies of the dynamics of problematic speech in a far-right Facebook
group, exemplifying its mutation from conservative to extreme. Finally, we
examine the dynamics of opinion emergence and co-occurrence, and we hint at
some pathways through which extreme opinions creep into the mainstream online
discourse.
