TY - GEN
T1 - What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
AU - Zeng, Jichuan
AU - Li, Jing
AU - He, Yulan
AU - Gao, Cuiyun
AU - Lyu, Michael
AU - King, Irwin
PY - 2020/4/20
Y1 - 2020/4/20
N2 - In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful - topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.
AB - In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful - topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.
KW - argumentation mining
KW - discourse modeling
KW - dynamic data processing
KW - social media
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85086598924&partnerID=8YFLogxK
U2 - 10.1145/3366423.3380223
DO - 10.1145/3366423.3380223
M3 - Conference article published in proceeding or book
AN - SCOPUS:85086598924
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 1502
EP - 1513
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery, Inc
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -