Abstract
Twitter has become one of the most import channels to spread latest scholarly information because of its fast information spread speed. How to predict whether a scholarly tweet will be retweeted is a key task in understanding the message propagation within large user communities. Hence, we present the real-time scholarly retweeting prediction system that retrieves scholarly tweets which will be retweeted. First, we filter scholarly tweets from tracking a tweet stream. Then, we extract Tweet Scholar Blocks indicating metadata of papers. At last, we combine scholarly features with the Tweet Scholar Blocks to predict whether a scholarly tweet will be retweeted. Our system outperforms chosen baseline systems. Additionally, our system has the potential to predict scientific impact in real-time.- Anthology ID:
- C18-2006
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico
- Editor:
- Dongyan Zhao
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–29
- Language:
- URL:
- https://aclanthology.org/C18-2006
- DOI:
- Cite (ACL):
- Zhunchen Luo and Xiao Liu. 2018. Real-time Scholarly Retweeting Prediction System. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 25–29, Santa Fe, New Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Real-time Scholarly Retweeting Prediction System (Luo & Liu, COLING 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C18-2006.pdf