Making "fetch" happen: The influence of social and linguistic context on the success of lexical innovations

In an online community, new words come and go: today's "lol" may be replaced by tomorrow's "lulz." Changes in online writing are usually studied as a social process, with lexical innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the system in which it takes part. To investigate the links between social and structural factors in language change, we undertake a large-scale analysis of lexical innovation in the online community Reddit. We find that dissemination across many linguistic contexts is a sign of success: words that appear in more linguistic contexts grow faster and live longer. Furthermore, social and context dissemination are complementary. By combining them, it is possible to predict which innovations will stick, and to forecast when the others will begin to decline.

[1]  A. Kroch.  Reflexes of grammar in patterns of language change , 1989, Language Variation and Change.

[2]  Lauren Squires.  Enregistering internet language , 2010, Language in Society.

[3]  T G Clark,et al.  Survival Analysis Part I: Basic concepts and first analyses , 2003, British Journal of Cancer.

[4]  Julia Hockenmaier,et al.  Beefmoves: Dissemination, Diversity, and Dynamics of English Borrowings in a German Hip Hop Forum , 2012, ACL.

[5]  M. de Rijke,et al.  Ad Hoc Monitoring of Vocabulary Shifts over Time , 2015, CIKM.

[6]  Lillian Lee,et al.  All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement , 2015, WWW.

[7]  Munmun De Choudhury,et al.  #thyghgapp: Instagram Content Moderation and Lexical Variation in Pro-Eating Disorder Communities , 2016, CSCW.

[8]  Bernd Kortmann,et al.  Analyzing lexical emergence in Modern American English online 1 , 2016, English Language and Linguistics.

[9]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[10]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[11]  Jacob Eisenstein,et al.  What to do about bad language on the internet , 2013, NAACL.

[12]  Stephen Pax Leonard,et al.  Language change and digital media: A review of conceptions and evidence , 2011 .

[13]  W. Kruskal.  Relative Importance by Averaging Over Orderings , 1987 .

[14]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[15]  Allan A. Metcalf.  Predicting New Words: The Secrets of Their Success , 2002 .

[16]  Brian MacWhinney,et al.  Competition and Lexical Categorization , 1987 .

[17]  Alan Partington.  Corpus Evidence of Language Change — The Case of the Intensifier , 1993 .

[18]  James A. Walker,et al.  The Present of the English Future: Grammatical Variation and Collocations in Discourse , 2009 .

[19]  Eric Gilbert,et al.  Widespread underprovision on Reddit , 2013, CSCW.

[20]  Scott Tonidandel,et al.  Determining the statistical significance of relative weights. , 2009, Psychological methods.

[21]  Sali A. Tagliamonte,et al.  Well weird, right dodgy, very strange, really cool: Layering and recycling in English intensifiers , 2003, Language in Society.

[22]  Mary Bucholtz,et al.  “Why be normal?”: Language and identity practices in a community of nerd girls , 1999, Language in Society.

[23]  Suzanne Stevenson,et al.  Automatically Identifying the Source Words of Lexical Blends in English , 2010, CL.

[24]  Brendan T. O'Connor,et al.  Demographic Dialectal Variation in Social Media: A Case Study of African-American English , 2016, EMNLP.

[25]  Matthew Rowe,et al.  Towards Modelling Language Innovation Acceptance in Online Social Networks , 2016, WSDM.

[26]  Jeffrey H Silber,et al.  Optimal multivariate matching before randomization. , 2004, Biostatistics.

[27]  D.,et al.  Regression Models and Life-Tables , 2022 .

[28]  Jure Leskovec,et al.  No country for old members: user lifecycle and linguistic change in online communities , 2013, WWW.

[29]  Leo Egghe.  Untangling Herdan's law and Heaps' law: Mathematical and informetric arguments , 2007, J. Assoc. Inf. Sci. Technol..

[30]  Patrick Drouin,et al.  When Terms Disappear from a Specialized Lexicon: A Semi-automatic Investigation into Necrology , 2011 .

[31]  Jacob Eisenstein.  Systematic patterning in phonologically‐motivated orthographic variation , 2015 .

[32]  Adilson E. Motter,et al.  Niche as a Determinant of Word Fate in Online Groups , 2010, PloS one.

[33]  Sali A. Tagliamonte,et al.  Not always variable: Probing the vernacular grammar , 2015, Language Variation and Change.

[34]  Ari Rappoport,et al.  Don't Let Me Be #Misunderstood: Linguistically Motivated Algorithm for Predicting the Popularity of Textual Memes , 2015, ICWSM.