You talk what you read: Understanding News Comment Behavior by
  Dispositional and Situational Attribution
Yuhang Wang Yuxiang Zhang Dongyuan Lu Jitao Sang
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.