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Target-specific stance detection on social media, which aims at classifying atextual data instance such as a post or a comment into a stance class of atarget issue, has become an emerging opinion mining paradigm of importance. Anexample application would be to overcome vaccine hesitancy in combating thecoronavirus pandemic. However, existing stance detection strategies rely merelyon the individual instances which cannot always capture the expressed stance ofa given target. In response, we address a new task called conversational stancedetection which is to infer the stance towards a given target (e.g., COVID-19vaccination) when given a data instance and its corresponding conversationthread. To tackle the task, we first propose a benchmarking conversationalstance detection (CSD) dataset with annotations of stances and the structuresof conversation threads among the instances based on six major social mediaplatforms in Hong Kong. To infer the desired stances from both data instancesand conversation threads, we propose a model called Branch-BERT thatincorporates contextual information in conversation threads. Extensiveexperiments on our CSD dataset show that our proposed model outperforms all thebaseline models that do not make use of contextual information. Specifically,it improves the F1 score by 10.3% compared with the state-of-the-art method inthe SemEval-2016 Task 6 competition. This shows the potential of incorporatingrich contextual information on detecting target-specific stances on socialmedia platforms and implies a more practical way to construct future stancedetection tasks.

Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads