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| Content Provider | ACM Digital Library |
|---|---|
| Author | Qiu, Minghui Jiang, Jing Yang, Liu |
| Abstract | Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods. |
| Starting Page | 873 |
| Ending Page | 878 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450322638 |
| DOI | 10.1145/2505515.2505634 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Side/stance identification Latent variable model Interaction features |
| Content Type | Text |
| Resource Type | Article |
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