Tracing Linguistic Markers of Influence in a Large Online Organisation (2023)

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.

## methodology we aim to understand the correlation between influence, as defined by either network-based centrality metrics (mail-based) or organisational role influence (role-based), and language usage in terms of linguistic traits. for each participant, we consider the emails they sent in a given time period and investigate correlations of certain features of their email text with two different measures of influence. liwc representation linguistic inquiry and word count @xcite is a well-recognised psycholinguistic lexicon; it provides word counts for 85 different linguistic, psychological, personal concern, and informal language marker categories. here, we aggregate the word counts within each linguistic category for each participant using the liwc 2015 dictionary (academic license), and normalise by the total number of emails sent by that participant. such a normalisation is more appropriate here than normalising by total number of words written, as many ietf emails include long technical sections. this generates a representation of a participant as their mean usage of each liwc category; while this is a relatively reduced, low-dimensional representation of a person's language, it has the advantage of being interpretable and psychologically well-motivated. ## experimental set-up dataset the ietf is organised in working groups (wgs). each wg has a technical focus (e.g., http wg for the http protocol) and one or more wg chairs. we use data from two public sources: the ietf mail archives foot_0 and the datatracker foot_1 . the mail archives cover wg activities, meetings, and administration. we gathered 2,106,804 emails from 56,733 email addresses spanning 2000-2019. to determine mail-based influence, we use a social graph based on mailing list interactions (messages from one person to another) as built by @xcite . we rank participants by their eigenvector centrality, a measure of a node's influence in a graph, and transform rank to a percentile. to determine role-based influence, we used datatracker for information about wg chairs and their tenure. ## results & discussion we now explore the results (see ## conclusions & future directions this paper explores the linguistic patterns of influence in an online collaborative organisation, by analysing the differences between high-and lowinfluence participants. using two aspects of influence -mail-based, derived from the email network, and organisational role-based -we were able to unfold several traits that differentiate influential participants from others. many of our findings seem corroborated by studies in organisational theory. we observed that influential people exhibit more collaborative and community-oriented traits, and also stronger signs of engagement in discussions. we also observed that as people go on to become influential participants, they evolve in their communication and are seen to be more engaging and descriptive in their linguistic style. an interesting practical application of our research is identifying and analyzing groups that are dysfunctional in terms of participant roles and their communication patterns (e.g., where the chair is not performing their role). in future work, we will extend the experiments to study these patterns of interaction in more linguistic depth, between more different roles within an organisation (possibly for multiple collaborative organisations). we will attempt to go beyond lexical count and account for word context.