@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.",
}