@inproceedings{luo-liu-2018-real,
title = "Real-time Scholarly Retweeting Prediction System",
author = "Luo, Zhunchen and
Liu, Xiao",
editor = "Zhao, Dongyan",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-2006/",
pages = "25--29",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Real-time Scholarly Retweeting Prediction System
%A Luo, Zhunchen
%A Liu, Xiao
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F luo-liu-2018-real
%X 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.
%U https://aclanthology.org/C18-2006/
%P 25-29
Markdown (Informal)
[Real-time Scholarly Retweeting Prediction System](https://aclanthology.org/C18-2006/) (Luo & Liu, COLING 2018)
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.