@inproceedings{amba-hombaiah-etal-2023-creator,
title = "Creator Context for Tweet Recommendation",
author = "Amba Hombaiah, Spurthi and
Chen, Tao and
Zhang, Mingyang and
Bendersky, Michael and
Najork, Marc and
Colen, Matt and
Levi, Sergey and
Ofitserov, Vladimir and
Amin, Tanvir",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.34",
doi = "10.18653/v1/2023.emnlp-industry.34",
pages = "353--363",
abstract = "When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet. In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case {--} recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.",
}
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<abstract>When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet. In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case – recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.</abstract>
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%0 Conference Proceedings
%T Creator Context for Tweet Recommendation
%A Amba Hombaiah, Spurthi
%A Chen, Tao
%A Zhang, Mingyang
%A Bendersky, Michael
%A Najork, Marc
%A Colen, Matt
%A Levi, Sergey
%A Ofitserov, Vladimir
%A Amin, Tanvir
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F amba-hombaiah-etal-2023-creator
%X When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet. In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case – recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.
%R 10.18653/v1/2023.emnlp-industry.34
%U https://aclanthology.org/2023.emnlp-industry.34
%U https://doi.org/10.18653/v1/2023.emnlp-industry.34
%P 353-363
Markdown (Informal)
[Creator Context for Tweet Recommendation](https://aclanthology.org/2023.emnlp-industry.34) (Amba Hombaiah et al., EMNLP 2023)
ACL
- Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork, Matt Colen, Sergey Levi, Vladimir Ofitserov, and Tanvir Amin. 2023. Creator Context for Tweet Recommendation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 353–363, Singapore. Association for Computational Linguistics.