TY - JOUR
T1 - Sociolinguistic Radar of Phonological Variation and Social Meaning
T2 - Variables, Quantitative Methods, and Prospects
AU - Wang, Wei
AU - Fan, Lili
AU - Wang, Yutong
AU - Ni, Qinghua
AU - Wang, Fei Yue
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024
Y1 - 2024
N2 - Inspired by the term "social radar,"which collects and processes information about social behaviors, this article proposed "sociolinguistic radar,"which represents an emerging branch in social investigation and evaluation system aiming to explore the dynamic correlation between sociophonetic variants and macrosociological categories, such as age, gender, ethnicity, and socioeconomic status. The classic quantitative methods in this field include sociolinguistic surveys and interview, quantitative sociophonetics, and social network analysis. These methods have been proved to be effective in tracking the cognitive correlates of phonological variables. Under the emerging framework of sociolinguistic radar, speakers are no longer passive carriers, but active agents in transforming linguistic styles in the process of forming social differentiations, thus contributing to the construction of new social meaning. With the advancement in neuroscience and artificial intelligence (AI), the neurosociolinguistic and AI-based sociolinguistic radar research will thrive and empower the scope and strength of detecting linguistic variation. The working mechanism of this emerging model leverages neural and AI tool packages to radar and analyze linguistic variation, communication patterns, and diverse sociolinguistic phenomena. This interdisciplinary approach combines the principles of sociolinguistics, which will thoroughly examine the relationship between language and society.
AB - Inspired by the term "social radar,"which collects and processes information about social behaviors, this article proposed "sociolinguistic radar,"which represents an emerging branch in social investigation and evaluation system aiming to explore the dynamic correlation between sociophonetic variants and macrosociological categories, such as age, gender, ethnicity, and socioeconomic status. The classic quantitative methods in this field include sociolinguistic surveys and interview, quantitative sociophonetics, and social network analysis. These methods have been proved to be effective in tracking the cognitive correlates of phonological variables. Under the emerging framework of sociolinguistic radar, speakers are no longer passive carriers, but active agents in transforming linguistic styles in the process of forming social differentiations, thus contributing to the construction of new social meaning. With the advancement in neuroscience and artificial intelligence (AI), the neurosociolinguistic and AI-based sociolinguistic radar research will thrive and empower the scope and strength of detecting linguistic variation. The working mechanism of this emerging model leverages neural and AI tool packages to radar and analyze linguistic variation, communication patterns, and diverse sociolinguistic phenomena. This interdisciplinary approach combines the principles of sociolinguistics, which will thoroughly examine the relationship between language and society.
KW - Artificial intelligence (AI)-based sociolinguistics
KW - linguistic variation
KW - neurosociolinguistics
KW - phonological variables
KW - social stratifications
KW - sociolinguistic radar
KW - sociophonetic variants
UR - https://www.scopus.com/pages/publications/85211604370
U2 - 10.1109/TCSS.2024.3432117
DO - 10.1109/TCSS.2024.3432117
M3 - Article
AN - SCOPUS:85211604370
SN - 2329-924X
VL - 11
SP - 7734
EP - 7741
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 6
ER -