  Detecting and labeling stance in social media text is strongly motivated by
hate speech detection, poll prediction, engagement forecasting, and concerted
propaganda detection. Today's best neural stance detectors need large volumes
of training data, which is difficult to curate given the fast-changing
landscape of social media text and issues on which users opine. Homophily
properties over the social network provide strong signal of coarse-grained
user-level stance. But semi-supervised approaches for tweet-level stance
detection fail to properly leverage homophily. In light of this, We present
SANDS, a new semi-supervised stance detector. SANDS starts from very few
labeled tweets. It builds multiple deep feature views of tweets. It also uses a
distant supervision signal from the social network to provide a surrogate loss
signal to the component learners. We prepare two new tweet datasets comprising
over 236,000 politically tinted tweets from two demographics (US and India)
posted by over 87,000 users, their follower-followee graph, and over 8,000
tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49)
on US (India)-based datasets, outperforming 17 baselines (including variants of
SANDS) substantially, particularly for minority stance labels and noisy text.
Numerous ablation experiments on SANDS disentangle the dynamics of textual and
network-propagated stance signals.
