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Qualitative research provides methodological guidelines for observing andstudying communities and cultures on online social media platforms. However,such methods demand considerable manual effort from researchers and can beoverly focused and narrowed to certain online groups. This work proposes acomplete solution to accelerate the qualitative analysis of problematic onlinespeech, focusing on opinions emerging from online communities by leveragingmachine learning algorithms. First, we employ qualitative methods of deepobservation for understanding problematic online speech. This initialqualitative study constructs an ontology of problematic speech, which containssocial media postings annotated with their underlying opinions. The qualitativestudy dynamically constructs the set of opinions, simultaneous with labelingthe postings. Next, we use keywords to collect a large dataset from threeonline social media platforms (Facebook, Twitter, and Youtube). Finally, weintroduce an iterative data exploration procedure to augment the dataset. Italternates between a data sampler -- which balances exploration andexploitation of unlabeled data -- the automatic labeling of the sampled data,the manual inspection by the qualitative mapping team, and, finally, theretraining of the automatic opinion classifiers. We present both qualitativeand quantitative results. First, we show that our human-in-the-loop methodsuccessfully augments the initial qualitatively labeled and narrowly focuseddataset and constructs a more encompassing dataset. Next, we present detailedcase studies of the dynamics of problematic speech in a far-right Facebookgroup, exemplifying its mutation from conservative to extreme. Finally, weexamine the dynamics of opinion emergence and co-occurrence, and we hint atsome pathways through which extreme opinions creep into the mainstream onlinediscourse.

Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions