You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution

Yuhang Wang, Yuxiang Zhang, Dongyuan Lu, Jitao Sang
8/4/2023
cs.CL

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Abstract

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.

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Cite this paper

@article{wang2023talk,
  title  = {You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution},
  author = {Yuhang Wang and Yuxiang Zhang and Dongyuan Lu and Jitao Sang},
  year   = {2023},
  eprint = {2308.02168},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.02168v1}
}

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