@inproceedings{xu-etal-2025-causally,
title = "Causally Modeling the Linguistic and Social Factors that Predict Email Response",
author = "Xu, Yinuo and
Chen, Hong and
Rakshit, Sushrita and
Ananthasubramaniam, Aparna and
Yadav, Omkar and
Zheng, Mingqian and
Jiang, Michael and
Zhang, Lechen and
Yi, Bowen and
Alkiek, Kenan and
Israeli, Abraham and
Shu, Bangzhao and
Shen, Hua and
Pei, Jiaxin and
Zhang, Haotian and
Schirmer, Miriam and
Jurgens, David",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.594/",
doi = "10.18653/v1/2025.naacl-long.594",
pages = "11842--11866",
ISBN = "979-8-89176-189-6",
abstract = "Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection."
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<abstract>Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection.</abstract>
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%0 Conference Proceedings
%T Causally Modeling the Linguistic and Social Factors that Predict Email Response
%A Xu, Yinuo
%A Chen, Hong
%A Rakshit, Sushrita
%A Ananthasubramaniam, Aparna
%A Yadav, Omkar
%A Zheng, Mingqian
%A Jiang, Michael
%A Zhang, Lechen
%A Yi, Bowen
%A Alkiek, Kenan
%A Israeli, Abraham
%A Shu, Bangzhao
%A Shen, Hua
%A Pei, Jiaxin
%A Zhang, Haotian
%A Schirmer, Miriam
%A Jurgens, David
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F xu-etal-2025-causally
%X Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection.
%R 10.18653/v1/2025.naacl-long.594
%U https://aclanthology.org/2025.naacl-long.594/
%U https://doi.org/10.18653/v1/2025.naacl-long.594
%P 11842-11866
Markdown (Informal)
[Causally Modeling the Linguistic and Social Factors that Predict Email Response](https://aclanthology.org/2025.naacl-long.594/) (Xu et al., NAACL 2025)
ACL
- Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, and David Jurgens. 2025. Causally Modeling the Linguistic and Social Factors that Predict Email Response. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 11842–11866, Albuquerque, New Mexico. Association for Computational Linguistics.