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 G.T. Healey, Matthew Purver

Main: Computational Social Science and Cultural Analytics Main-poster Paper

Poster Session 5: Computational Social Science and Cultural Analytics (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 16:15-17:45 (EDT) (America/Toronto)
Global Time: July 11, Poster Session 5 (20:15-21:45 UTC)
Keywords: human behavior analysis, sociolinguistics
TLDR: 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 Fo...
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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.