@inproceedings{khare-etal-2023-tracing,
title = "Tracing Linguistic Markers of Influence in a Large Online Organisation",
author = "Khare, Prashant and
Shekhar, Ravi and
Karan, Mladen and
McQuistin, Stephen and
Perkins, Colin and
Castro, Ignacio and
Tyson, Gareth and
Healey, Patrick and
Purver, Matthew",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.8",
doi = "10.18653/v1/2023.acl-short.8",
pages = "82--90",
abstract = "Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. Here, we investigate a similar question in a large, distributed, consensus-driven community with little traditional power hierarchy {--} the Internet Engineering Task Force (IETF), a collaborative organisation that designs internet standards. Our analysis based on lexical categories (LIWC) and BERT, shows that participants{'} levels of influence can be predicted from their email text, and identify key linguistic differences (e.g., certain LIWC categories, such as {``}WE{''} are positively correlated with high-influence). We also identify the differences in language use for the same person before and after becoming influential.",
}
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<abstract>Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. Here, we investigate a similar question in a large, distributed, consensus-driven community with little traditional power hierarchy – the Internet Engineering Task Force (IETF), a collaborative organisation that designs internet standards. Our analysis based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identify key linguistic differences (e.g., certain LIWC categories, such as “WE” are positively correlated with high-influence). We also identify the differences in language use for the same person before and after becoming influential.</abstract>
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%0 Conference Proceedings
%T Tracing Linguistic Markers of Influence in a Large Online Organisation
%A Khare, Prashant
%A Shekhar, Ravi
%A Karan, Mladen
%A McQuistin, Stephen
%A Perkins, Colin
%A Castro, Ignacio
%A Tyson, Gareth
%A Healey, Patrick
%A Purver, Matthew
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F khare-etal-2023-tracing
%X Social science and psycholinguistic research have shown that power and status affect how people use language in a range of domains. Here, we investigate a similar question in a large, distributed, consensus-driven community with little traditional power hierarchy – the Internet Engineering Task Force (IETF), a collaborative organisation that designs internet standards. Our analysis based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identify key linguistic differences (e.g., certain LIWC categories, such as “WE” are positively correlated with high-influence). We also identify the differences in language use for the same person before and after becoming influential.
%R 10.18653/v1/2023.acl-short.8
%U https://aclanthology.org/2023.acl-short.8
%U https://doi.org/10.18653/v1/2023.acl-short.8
%P 82-90
Markdown (Informal)
[Tracing Linguistic Markers of Influence in a Large Online Organisation](https://aclanthology.org/2023.acl-short.8) (Khare et al., ACL 2023)
ACL
- Prashant Khare, Ravi Shekhar, Mladen Karan, Stephen McQuistin, Colin Perkins, Ignacio Castro, Gareth Tyson, Patrick Healey, and Matthew Purver. 2023. Tracing Linguistic Markers of Influence in a Large Online Organisation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 82–90, Toronto, Canada. Association for Computational Linguistics.