Predicting Stance Polarity and Intensity in Cyber Argumentation With Deep Bidirectional Transformers

In online deliberation, participants argue in support or opposition to one another's arguments and ideas to advocate their position. Often their stance expressed in their posts is implicit and must be derived from the post's text. Existing stance detection models predict the polarity of the user's stance from the text, but do not consider the stance's intensity. We introduce a new research problem, stance polarity, and intensity prediction in response relationships between posts. This problem seeks to predict both the stance polarity and intensity of a replying post toward its parent post in online deliberation. Using our cyber argumentation platform, we have collected an empirical data set with explicitly labeled stance polarity and intensity relationships. In this work, we create six models: five are adapted from top-performing stance detection models and another novel model that fine-tunes the deep bidirectional transformer model BERT. We train and test these six models on our empirical data set to compare their performance for stance polarity and intensity prediction and stance detection. Our results demonstrate that our method of encoding the stance polarity and intensity labels allows the models to predict stance polarity and intensity without compromising their accuracy for stance detection, making these models more versatile. Our results reveal that a novel split architecture for fine-tuning the BERT model outperforms the other models for stance polarity and intensity prediction by 5% accuracy. This work is the first to train models for predicting both the stance polarity and intensity in one combined task while maintaining good accuracy.