Re-entry Prediction for Online Conversations via Self-Supervised Learning
This work addresses the problem of helping users track discussions they wish to continue on social media, representing an incremental improvement by introducing self-supervised learning to an existing task.
The paper tackles the re-entry prediction problem in online conversations by proposing three self-supervised auxiliary tasks to capture conversation patterns and user engagement, resulting in outperforming previous state-of-the-art methods with fewer parameters and faster convergence on Twitter and Reddit datasets.
In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.