Competition and Selection Among Conventions

Abstract

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the on-line domain, where the ease of information transmission makes them particularly forceful, and where the available data offers the potential to study competition among conventions at a fine-grained level.In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis of the overall dynamics must take place in the presence of these two issues.In this work we study a setting in which we can cleanly track the competition among conventions while explicitly taking these sources of complexity into account. Our analysis is based on the spread of low-level authoring conventions in the e-print arXiv over 24 years and roughly a million posted papers: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of conventions; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time -and at the individual level about the outcome of "fights" between people over convention choices.

Short-Term Meaning Shift: A Distributional Exploration

“…Unlike most previous work, we focus on the language of online communities. Recent studies of this type of language have investigated the spread of new forms and meanings Fernández, 2017, 2018;Stewart and Eisenstein, 2018), competing lexical variants (Rotabi et al, 2017), and the relation between conventions in a community and the social standing of its members (Danescu-Niculescu-Mizil et al, 2013). None of these works has analyzed the ability of a distributional model to capture these phenomena, which is what we do in this paper for short-term meaning shift.…”

A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains

“…The only notable work for explicitly measuring across domain meaning shifts is Ferrari et al (2017), which is based on semantic vector spaces and cosine distance. Synchronic LSC across communities has been investigated as meaning variation in online communities, leverag-ing the large-scale data which has become available thanks to online social platforms (Del Tredici and Fernández, 2017;Rotabi et al, 2017).…”

Library Adoption Dynamics in Software Teams

“…Less experienced researchers tended to win invisible fights, i.e., fights over T E X-macros that did not have a high visibility. More experienced researchers, e.g., advisors and senior PIs, tended to win highly visible fights such as fights over the conventions used in the title of the paper [24]. In the software development paradigm, norms tend to develop in software teams, to which developers eventually learn and conform [25].…”

“…Two options are to count 1) the number of commits of a user (in any project) or 2) the time elapsed since the users first commit (in any project). Although these two options obtain similar results, the current work maintains the standard set by prior studies [24] and therefore defines experience as the time since the user's first commit (in any project). We observe in Fig.…”