The ease of internet access has significantly increased the number of user comments authored by inexperienced writers. Despite the potential usefulness of such comments, readers face the daunting task of sifting through copious amounts of uninformative content to extract relevant information. One popular approach to deal with this problem is to build systems that can help readers by recommending helpful comments or summarizing available information. We, however, consider the problem from the perspective of commenters: Can we build a system that can guide commenters to write “better” comments? Such an approach would enhance the overall quality of textual content available online and complement existing solutions for reader assistance.

In this talk, I will present the core components of an automated system to assist commenters in constructing better-structured arguments in their comments. These include: (1) A theoretical argumentation model to capture the evaluability of arguments in the online setting, (2) A classifier for determining an appropriate type of support---reason or evidence---for propositions comprising user comments, (3) A classifier for identifying support relations present in user comments. I will also discuss how this system can be applied to practical applications such as assistive commenting interfaces and recommendation systems.

Joonsuk Park is an assistant professor in the Department of Computer Science at the University of Richmond, where he is also affiliated with the Linguistics Program. His research primarily focuses on theoretical and empirical methods to assist human communication from the perspectives of logicality, factuality, and ethicality. Specific areas of research include argument mining, fact verification, and ethics. Currently, he is a visiting scholar at NAVER AI Lab and a co-organizer of the 10th Workshop on Argument Mining (ArgMining). Previously, he was a visiting assistant professor in the Department of Computer Science at Williams College.