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Unsupervised User Stance Detection on Twitter
We present a highly effective unsupervised framework for detecting the stance
of prolific Twitter users with respect to controversial topics. In particular,
we use dimensionality reduction to project users onto a low-dimensional space,
followed by clustering, which allows us to find core users that are
representative of the different stances. Our framework has three major
advantages over pre-existing methods, which are based on supervised or
semi-supervised classification. First, we do not require any prior labeling of
users: instead, we create clusters, which are much easier to label manually
afterwards, e.g., in a matter of seconds or minutes instead of hours. Second,
there is no need for domain- or topic-level knowledge either to specify the
relevant stances (labels) or to conduct the actual labeling. Third, our
framework is robust in the face of data skewness, e.g., when some users or some
stances have greater representation in the data. We experiment with different
combinations of user similarity features, dataset sizes, dimensionality
reduction methods, and clustering algorithms to ascertain the most effective
and most computationally efficient combinations across three different datasets
(in English and Turkish). We further verified our results on additional tweet
sets covering six different controversial topics. Our best combination in terms
of effectiveness and efficiency uses retweeted accounts as features, UMAP for
dimensionality reduction, and Mean Shift for clustering, and yields a small
number of high-quality user clusters, typically just 2--3, with more than 98\%
purity. The resulting user clusters can be used to train downstream
classifiers. Moreover, our framework is robust to variations in the
hyper-parameter values and also with respect to random initialization
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