"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space
This work addresses the challenge of understanding conflict dynamics in online discourse for researchers and practitioners in computational linguistics, though it is incremental as it builds on existing methods for sarcasm and argument detection.
The study tackled the problem of detecting arguments in online interactions by investigating how sarcasm influences disagreement classification, and found that modeling sarcasm improved performance in classifying argumentative relations (agree/disagree/none) across all experimental setups.
Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack the opponent by an ad hominem argument. To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures). We demonstrate that modeling sarcasm improves the argumentative relation classification task (agree/disagree/none) in all setups.