You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution
This addresses the challenge of accurately modeling user comments in news platforms, which is incremental by integrating existing factors into a new framework.
The paper tackled the problem of understanding news comment behavior by considering both users' interaction history (dispositional factors) and the news content (situational factors), proposing a three-part encoder-decoder framework that improved applications like reader-aware news summarization and aspect-opinion forecasting.
Many news comment mining studies are based on the assumption that comment is explicitly linked to the corresponding news. In this paper, we observed that users' comments are also heavily influenced by their individual characteristics embodied by the interaction history. Therefore, we position to understand news comment behavior by considering both the dispositional factors from news interaction history, and the situational factors from corresponding news. A three-part encoder-decoder framework is proposed to model the generative process of news comment. The resultant dispositional and situational attribution contributes to understanding user focus and opinions, which are validated in applications of reader-aware news summarization and news aspect-opinion forecasting.