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The social Web is a widely used platform for online discussion. Across socialmedia, users can start discussions by posting a topical image, url, or message.Upon seeing this initial post, other users may add their own comments to thepost, or to another user's comment. The resulting online discourse produces acomment thread, which constitutes an enormous portion of modern onlinecommunication. Comment threads are often viewed as trees: nodes represent thepost and its comments, while directed edges represent reply-to relationships.The goal of the present work is to predict the size and shape of these commentthreads. Existing models do this by observing the first several comments andthen fitting a predictive model. However, most comment threads are relativelysmall, and waiting for data to materialize runs counter to the goal of theprediction task. We therefore introduce the Comment Thread Prediction Model(CTPM) that accurately predicts the size and shape of a comment thread usingonly the text of the initial post, allowing for the prediction of new postswithout observable comments. We find that the CTPM significantly outperformsexisting models and competitive baselines on thousands of Reddit discussionsfrom nine varied subreddits, particularly for new posts.

Modelling Online Comment Threads from their Start