Weakly supervised contrastive representation learning to encode narrative viewpoint of COVID-19 tweets

Viewpoint detection is a crucial step for characterizing online narratives and understanding their diffusion and evolution across online social spaces. Existing methods for semantic understanding of online posts have shown strong progress towards grouping documents with similar topics but struggle to differentiate between different viewpoints towards those topics. The purpose of this work is to infuse semantic embedding spaces with improved viewpoint information under the constraint of low data availability. To address this task, we develop a novel weakly supervised contrastive learning approach that leverages social proximity of users as a self-supervised signal of shared viewpoint likelihood. We demonstrate the utility of this method on a use case of X (formerly Twitter) discussion related to COVID-19. We show that fine-tuned embeddings which were trained to predict social proximity signals present in retweet networks demonstrate the capability to infuse learned embeddings with viewpoint information. Finally, we demonstrate that these viewpoint-infused embeddings show improved effectiveness at identifying clusters of tweets with shared viewpoints and topics when used in a topic modeling pipeline. Such viewpoint-infused embeddings have strong potential to support multiple semantic reasoning tasks including topic modeling, stance detection, and narrative detection.