Abstract

Just as the meaning of words is tied to the communities in which they are used, so too is semantic change. But how does lexical semantic change manifest differently across different communities? In this work, we investigate the relationship between community structure and semantic change in 45 communities from the social media website Reddit. We use distributional methods to quantify lexical semantic change and induce a social network on communities, based on interactions between members. We explore the relationship between semantic change and the clustering coefficient of a community’s social network graph, as well as community size and stability. While none of these factors are found to be significant on their own, we report a significant effect of their three-way interaction. We also report on significant word-level effects of frequency and change in frequency, which replicate previous findings.

Highlights

  • The mechanisms and patterns of semantic change have a long history of study in linguistics (e.g., Paul, 1886; Bloomfield, 1933; Blank, 1999)

  • We found that all word-level fixed effects and their three-way interaction were significant at p

  • By looking at online communities, we were able to compute a clustering coefficient on the social network graph of each community, as well as several other community-level structural features

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Summary

Introduction

The mechanisms and patterns of semantic change have a long history of study in linguistics (e.g., Paul, 1886; Bloomfield, 1933; Blank, 1999). Historical accounts of semantic change typically consider meaning at the language level and, as Clark (1996) points out, referring to Lewis’s (1969) account of convention, the meaning of a word “does not hold for a word simpliciter, but for a word in a particular community”. We are able to define and compute several community-level structural characteristics including size, stability, and social network clustering (Section 5). Another way that our work differs from the variationist approach is that we consider change on the level of meaning. We use a multistage linear mixed effects statistical model to test the effect of various community features on wordlevel semantic change

Related work
Naıve cosine change
Diachronic SGNS
Rectified change score
Community features
D K aS a P a a
Social network model
Predictive model
Detecting multicollinearity
Results
Discussion and conclusions
A Subreddit selection
B Data preprocessing
C Vocabulary and SGNS training proceedure
Full Text
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