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Sociolinguistic Variation and Enregisterment in an Online Community of Practice: a Case Study of Metafilter.Com Sociolinguistic Variation and Enregisterment in an Online Community of Practice: A Case Study of MetaFilter.com Kimberly Ann Witten PhD University of York Language and Linguistic Science September 2014 I Abstract With the emergence of communities that are primarily based in computer-mediated communication (CMC) environments, we see the prevalence of internet-derived neologisms, i.e., netologisms. Often these netologisms are acronyms (e.g., ‘LOL’), blends (e.g., ‘weblog’), or other forms of abbreviation. These new forms may present challenges for English phonotactics, which must be spontaneously resolved by first-time speakers of the netologisms. If the forms contain orthographic characters or sequences that do not directly or consistently correlate to specific English phonemes or phoneme sequences, it is likely that these new forms display phonetic variation. Netologisms can also be used as linguistic resources in taking stances or asserting aspects of identity, especially where phonetic variation is possible. These stances may represent the identity of the group, or they may become associated with particular identities within the group. The process by which sounds, features and word forms become associated with particular identities is known as enregisterment (Agha, 2003, 2005; Squires, 2010). Enregisterment has traditionally been studied in sociolinguistics as a function of individuals interacting in face-to-face (FtF) environments (Johnstone, Andrus and Danielson, 2006; Beal, 2009). However, as more of our daily interactions are mediated by computers and technology, attention must be paid to how enregisterment may take place in primarily text-based social environments. This research presents the first large-scale mixed-methods study of enregisterment occurring in CMC. The varying pronunciations of two netologisms — the community’s nickname (‘MeFi’, from MetaFilter.com) and the collective nickname for its participants (‘MeFites’) — are naturally-occurring sociolinguistic variables that showcase the ongoing negotiation of community conventions and the development of group identity. An exploration of this kind adds an important piece to our broader understanding of linguistic interaction in CMC, while also exhibiting one of the many new directions of sociolinguistic research today. II II Table of Contents Abstract . II Table of Contents . III List of Figures . VII List of Illustrations . IX List of Examples . X Acknowledgements . XI Author’s declaration . XIII Chapter 1: Introduction . 1 1.1 Background . 2 1.2 Research Questions . 5 1.2.1 Hypotheses and Expected Findings . 5 1.3 Relevance and Broader Implications . 7 1.4 Thesis Structure . 7 Chapter 2: Literature Review . .. 9 2.1 Introduction to Literature Review . 9 2.2 Models of Community . 9 2.2.1 Community of Practice — Definition . 9 2.2.2 Community of Practice versus The Speech Community . .. 16 2.2.3 Social Networks and Weak Ties . 17 2.3 Capital . 19 2.3.1 Economic Capital . 19 2.3.2 Cultural Capital . .. 19 2.3.3 Social Capital . 20 2.3.4 Virtual Capital . 20 2.3.5 Summary of Capital . 20 2.4 Computer-Mediated Communication (CMC) . 21 2.4.1 Classification of CMC Research Areas . 21 2.4.2 Communicative Benefits of CMC . .. 22 2.4.3 MetaFilter as a Social Network Site, or …? . 23 2.4.4 Describing the Features of an Online Community . 25 2.5 Registers and Enregisterment . 25 2.5.1 Registers – Definition . 25 2.5.2 Enregisterment – Definition . 27 2.5.3 Indexicality . 28 2.5.4 Sociolinguistic Approaches to Variation and Enregisterment . 32 2.5.5 Enregisterment of a Language Variety . .. 32 2.5.6 Enregisterment of Forms Within a Variety . 37 2.5.7 Summary of Enregisterment . 39 2.6 Onomastics . 40 2.6.1 Overview of Naming Categories . 40 2.6.2 Place Names . 41 2.6.3 Personal Names . 44 2.6.4 Product and Brand Names . 45 2.7 Summary of Literature Review . 47 III III Chapter 3: MetaFilter and the M-Set Variables . 48 3.1 Introduction to MetaFilter and the M-Set Variables . 48 3.2 The MetaFilter Community — Culture and Context . 48 3.2.1 The MetaFilter Subsites . 48 3.2.2 A CMDA Outline of MetaFilter . 55 3.2.3 The MetaFilter Userbase . 61 3.2.4 MetaFilter Comment and Post Frequency . 62 3.3 Previous Research on MetaFilter . 64 3.3.1 Previous Study of MetaFilter as a CoP . 64 3.3.2 Previous Study of MetaFilter and Capital . 65 3.3.3 Previous Study of MetaFilter Ethos and Identity . 68 3.3.4 Summary of Previous Studies of MetaFilter . 70 3.4 The MetaFilter Register . 70 3.4.1 Elements of the MetaFilter Register . 71 3.5 A Linguistic Overview of the M-Set Variables . 73 3.5.1 The Structure & Composition of ‘MetaFilter’ . 75 3.5.2 Orthographic Features . 76 3.5.3 Grapheme-Phoneme Correspondence (GPC) Rules . 76 3.5.4 Phonetic Realizations of the M-Set . 79 3.5.5 Stress Assignment and Vowel Length . 85 3.5.6 Syllabification . 86 3.5.7 Morphological Processes . 87 3.6 Summary of MetaFilter and the M-Set . 88 Chapter 4: Methodology . 89 4.1 Introduction to Methodology . 89 4.2 Research Design . 89 4.2.1 The Research Approach . 89 4.2.2 The Researcher’s Perspective . 90 4.2.3 The Researcher’s Influence on Enregisterment . 91 4.2.4 Overview of the Types of Data Collected . .. 92 4.2.5 Online Surveys . 92 4.2.6 Participation and Usage Meta-Data . 94 4.2.7 Corpora — Word Frequency Tables . 95 4.2.8 Qualitative Data from Community Discourse – Posts and Comments . 95 4.3 Sampling Design . 96 4.4 Measures . .. 97 4.4.1 Measuring the Distribution of the M-Set . 98 4.4.2 Demographic Measures . 98 4.4.3 Measures of Social Engagement . 98 4.4.4 Measures of Metalinguistic Awareness . 98 4.4.5 M-Set Stance Measures . 99 4.4.6 Other Measures . 100 4.4.7 Summary of Measures . 100 4.5 Data Collection Procedures . 100 4.5.1 The MetaFilter Surveys . 101 4.5.2 InfoDump Collection Procedures . 106 4.5.3 Word Frequency Table Collection Procedures . 106 4.5.4 MetaTalk Thread Collection Procedures . 106 4.6 Data Analysis Procedures . 107 4.6.1 Data Correction and Adjustment . 107 IV IV 4.6.2 Quantitative and Qualitative Analyses . 109 4.7 Summary of Methodology . 109 Chapter 5: Data Results . 110 5.1 Introduction to Data Results . 110 5.2 The Distribution of the M-Set . 111 5.2.1 The Pronunciation of ‘MeFi’ — Survey Data . 111 5.2.2 The Pronunciation of ‘MeFite’ — Survey Data . 112 5.2.3 The Pronunciation of the M-Set — Panel Data . 112 5.2.4 Changes in M-Set Distributions Over Time . 113 5.3 Metalinguistic Awareness Factors . 118 5.3.1 Exclusivity of Use of Preferred M-Set Variants . 118 5.3.2 Amount of Thought Given to the Pronunciation of the M-Set . 123 5.3.3 Other Measures of Metalinguistic Awareness . 124 5.3.4 Analogies in Survey Rationales . 127 5.4 Demographic Factors . 131 5.4.1 Language Background and Experience . 132 5.4.2 Geography . 137 5.4.3 Age . 145 5.4.4 Gender . 150 5.5 Summary of Data Results . 151 Chapter 6: Enregisterment . 153 6.1 Introduction to Enregisterment . 153 6.2 The Enregisterment Process in CMC . 153 6.3 Message Chains . 155 6.3.1 Message Chain Components . 156 6.3.2 MetaFilter Message Chains and the Frequency of ‘MeFi’ . 157 6.4 Social Engagement Factors and the Enregisterment of the M-Set . 161 6.4.1 Year of Joining MetaFilter . 164 6.4.2 Frequency of Visitation to MetaFilter Subsites . 165 6.4.3 MetaFilter Site Participation and Enregisterment . 172 6.4.4 Frequency of Podcast Listening . ..
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