@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.",
}

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.
Khare, Prashant Shekhar, Ravi Karan, Mladen McQuistin, Stephen Perkins, Colin Castro, Ignacio Tyson, Gareth Healey, Patrick Purver, Matthew
Tracing Linguistic Markers of Influence in a Large Online Organisation