Persuasive Influence Detection: The Role of Argument Sequencing
Journal Article
·
· Proceedings of the AAAI Conference on Artificial Intelligence
- Columbia Univ., New York, NY (United States); Columbia University
- Columbia Univ., New York, NY (United States)
Automatic detection of persuasion in online discussion is key to understanding how social media is used. Predicting persuasiveness is difficult, however, due to the need to model world knowledge, dialogue, and sequential reasoning. Here, we focus on modeling the sequence of arguments in social media posts using neural models with embeddings for words, discourse relations, and semantic frames. We demonstrate significant improvement over prior work in detecting successful arguments. We also present an error analysis assessing novice human performance at predicting persuasiveness.
- Research Organization:
- Columbia Univ., New York, NY (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- EE0007684
- OSTI ID:
- 2281908
- Journal Information:
- Proceedings of the AAAI Conference on Artificial Intelligence, Journal Name: Proceedings of the AAAI Conference on Artificial Intelligence Journal Issue: 1 Vol. 32; ISSN 2159-5399
- Publisher:
- Association for the Advancement of Artificial IntelligenceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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