TY - GEN
T1 - Causally Modeling the Linguistic and Social Factors that Predict Email Response
AU - Xu, Yinuo
AU - Chen, Hong
AU - Rakshit, Sushrita
AU - Ananthasubramaniam, Aparna
AU - Yadav, Omkar
AU - Zheng, Mingqian
AU - Jiang, Michael
AU - Zhang, Lechen
AU - Yi, Bowen
AU - Alkiek, Kenan
AU - Israeli, Abraham
AU - Shu, Bangzhao
AU - Shen, Hua
AU - Pei, Jiaxin
AU - Zhang, Haotian
AU - Schirmer, Miriam
AU - Jurgens, David
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105027463617
U2 - 10.18653/v1/2025.naacl-long.594
DO - 10.18653/v1/2025.naacl-long.594
M3 - Conference contribution
AN - SCOPUS:105027463617
T3 - Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies: Long Papers, NAACL-HLT 2025
SP - 11842
EP - 11866
BT - Long Papers
A2 - Chiruzzo, Luis
A2 - Ritter, Alan
A2 - Wang, Lu
PB - Association for Computational Linguistics (ACL)
T2 - 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2025
Y2 - 29 April 2025 through 4 May 2025
ER -