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

arXiv: https://arxiv.org/abs/2308.02168 · HTML: https://mlsanity.com/paper/2308.02168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Automatic Article Commenting: the Task and Dataset](https://mlsanity.com/paper/1805.03668/) (2018)
- [Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model](https://mlsanity.com/paper/1906.01231/) (2019)
- [Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection](https://mlsanity.com/paper/2102.06856/) (2021)

## Abstract (arXiv)

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.