A Theoretically Grounded Approach to Summarizing Conversation Dynamics for Forecasting the Derailment of Online Conversations

Conversation derailment prediction represents a new paradigm of toxicity detection, where a system predicts from the start of a conversation whether it will derail into toxic exchanges, allowing moderators and users to act preemptively before harm is done. This approach requires a deep understanding of conversation dynamics. Previous work relies on linguistic features rooted in linguistic and social theories. While these features provide signals of conversation dynamics, they are exploratory in nature and potentially reflect a fraction of the overall pragmatic devices shaping the conversation trajectory. To capture the pragmatic dimension of conversations systematically, we start with a framework for annotating pragmatic information of conversations systematically and design summary generation methods to capture conversation trajectory dynamically. We achieve about 10% performance increase over a simple baseline, and 6.47% increase over a strong baseline on a dataset, and a slight performance increase on a benchmark dataset for the task of summarizing conversation dynamics.