Tracing Linguistic Markers of Influence in a Large Online Organisation

Prashant Khare; Ravi Shekhar; Mladen Karan; Stephen McQuistin; Colin Perkins; Ignacio Castro; Gareth Tyson; Patrick Healey; Matthew Purver

Tracing Linguistic Markers of Influence in a Large Online Organisation

Prashant Khare, Ravi Shekhar, Mladen Karan, Stephen McQuistin, Colin Perkins, Ignacio Castro, Gareth Tyson, Patrick Healey, Matthew Purver

School of Computer Science

Research output: Contribution to conference › Paper › peer-review

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 – the Internet Engineering Task Force (IETF), a collaborative organisation that develops technical standards for the Internet. Our analysis, based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identifies 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

Original language	English
Publication status	Published - 9 Jul 2023
Event	The 61st Annual Meeting of the Association for Computational Linguistics - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 61 https://2023.aclweb.org

Conference

Conference	The 61st Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL
Country/Territory	Canada
City	Toronto
Period	9/07/23 → 14/07/23
Internet address	https://2023.aclweb.org

Cite this

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title = "Tracing Linguistic Markers of Influence in a Large Online Organisation",

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 – the Internet Engineering Task Force (IETF), a collaborative organisation that develops technical standards for the Internet. Our analysis, based on lexical categories (LIWC) and BERT, shows that participants{\textquoteright} levels of influence can be predicted from their email text, and identifies 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",

author = "Prashant Khare and Ravi Shekhar and Mladen Karan and Stephen McQuistin and Colin Perkins and Ignacio Castro and Gareth Tyson and Patrick Healey and Matthew Purver",

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T1 - Tracing Linguistic Markers of Influence in a Large Online Organisation

AU - Khare, Prashant

AU - Shekhar, Ravi

AU - Karan, Mladen

AU - McQuistin, Stephen

AU - Perkins, Colin

AU - Castro, Ignacio

AU - Tyson, Gareth

AU - Healey, Patrick

AU - Purver, Matthew

N1 - Conference code: 61

PY - 2023/7/9

Y1 - 2023/7/9

N2 - 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 – the Internet Engineering Task Force (IETF), a collaborative organisation that develops technical standards for the Internet. Our analysis, based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identifies 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

AB - 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 – the Internet Engineering Task Force (IETF), a collaborative organisation that develops technical standards for the Internet. Our analysis, based on lexical categories (LIWC) and BERT, shows that participants’ levels of influence can be predicted from their email text, and identifies 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

M3 - Paper

T2 - The 61st Annual Meeting of the Association for Computational Linguistics

Y2 - 9 July 2023 through 14 July 2023

ER -

Tracing Linguistic Markers of Influence in a Large Online Organisation

Abstract

Conference

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