While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks – when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare-class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.

We address dissonant stance detection, classifying conflicting stance between two input statements. Computational models for traditional stance detection have typically been trained to indicate pro/con for a given target topic (e.g. gun control) and thus do not generalize well to new topics. In this paper, we systematically evaluate the generalizability of dissonant stance detection to situations where examples of the topic have not been seen at all or have only been seen a few times. We show that dissonant stance detection models trained on only 8 topics, none of which are the target topic, can perform as well as those trained only on a target topic. Further, adding non-target topics boosts performance further up to approximately 32 topics where accuracies start to plateau. Taken together, our experiments suggest dissonant stance detection models can generalize to new unanticipated topics, an important attribute for the social scientific study of social media where new topics emerge daily.