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Towards Modelling Language Innovation Acceptance in Online Social Networks

Authors:

Daniel Kershaw,

Patrick StaceyAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 553 - 562

https://doi.org/10.1145/2835776.2835784

Published: 08 February 2016 Publication History

Abstract

Language change and innovation is constant in online and offline communication, and has led to new words entering people's lexicon and even entering modern day dictionaries, with recent additions of 'e-cig' and 'vape'. However the manual work required to identify these 'innovations' is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN's (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simple statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied - variation in; frequency, form and meaning. Each show different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allowing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example innovations included 'casualidad' and 'cym'.

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Cited By

Spina SBrasolin PFranzini G(2024)Detecting emerging vocabulary in a large corpus of Italian tweetsResearch in Corpus Linguistics10.32714/ricl.13.01.0713:1(139-170)Online publication date: 2024
https://doi.org/10.32714/ricl.13.01.07
Ziems CHeld WShaikh OChen JZhang ZYang D(2023)Can Large Language Models Transform Computational Social Science?Computational Linguistics10.1162/coli_a_0050250:1(237-291)Online publication date: 1-Mar-2023
https://doi.org/10.1162/coli_a_00502
Tarrade LMagué JChevrot J(2022)Detecting and categorising lexical innovations in a corpus of tweetsPsychology of Language and Communication10.2478/plc-2022-1526:1(313-329)Online publication date: 21-Oct-2022
https://doi.org/10.2478/plc-2022-15
Show More Cited By

Index Terms

Towards Modelling Language Innovation Acceptance in Online Social Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Published In

cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 08 February 2016

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Engineering and Physical Sciences Research Council

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Spina SBrasolin PFranzini G(2024)Detecting emerging vocabulary in a large corpus of Italian tweetsResearch in Corpus Linguistics10.32714/ricl.13.01.0713:1(139-170)Online publication date: 2024
https://doi.org/10.32714/ricl.13.01.07
Ziems CHeld WShaikh OChen JZhang ZYang D(2023)Can Large Language Models Transform Computational Social Science?Computational Linguistics10.1162/coli_a_0050250:1(237-291)Online publication date: 1-Mar-2023
https://doi.org/10.1162/coli_a_00502
Tarrade LMagué JChevrot J(2022)Detecting and categorising lexical innovations in a corpus of tweetsPsychology of Language and Communication10.2478/plc-2022-1526:1(313-329)Online publication date: 21-Oct-2022
https://doi.org/10.2478/plc-2022-15
Ningtyas AHanbury APiroi FAndersson L(2021)Data Augmentation for Layperson’s Medical Entity Linking TaskProceedings of the 13th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3503162.3503172(99-106)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.1145/3503162.3503172
Toth AConroy ECampbell M(2021)Beyond action video games: Differences in gameplay and ability preferences among gaming genresEntertainment Computing10.1016/j.entcom.2021.10040838(100408)Online publication date: May-2021
https://doi.org/10.1016/j.entcom.2021.100408
Mahler H(2019)Lexical Emergence on Reddit: An Analysis of Lexical Change on the “Front Page of the Internet”Lexis10.4000/lexis.4917Online publication date: 2-Sep-2019
https://doi.org/10.4000/lexis.4917
Nguyen DDoğruöz ARosé Cde Jong F(2016)Computational sociolinguisticsComputational Linguistics10.1162/COLI_a_0025842:3(537-593)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1162/COLI_a_00258

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