Using Network Science to Map What Montréal Bilinguals Talk about Across Languages and Communicative Contexts

Abstract

Recent work within the language sciences, particularly bilingualism, has sought new methods to evaluate and characterize how people differentially use language across different communicative contexts. These differences have thus far been linked to changes in cognitive control strategy, reading behavior, and brain organization. Here, we approach this issue using a novel application of Network Science to map the conversational topics that Montréal bilinguals discuss across communicative contexts (e.g., work, home, family, school, social), in their dominant vs. non-dominant language. Our results demonstrate that all communicative contexts display a unique pattern in which conversational topics are discussed, but only a few communicative contexts (work and social) display a unique pattern of how many languages are used to discuss particular topics. We also demonstrate that the dominant language has greater network size, strength, and density than the non-dominant language, suggesting that more topics are used in a wider variety of contexts in this language. Lastly, using community detection to thematically group the topics in each language, we find evidence of greater specificity in the non-dominant language than the dominant language. We contend that Network Science is a valuable tool for representing complex information, such as individual differences in bilingual language use, in a rich and granular manner, that may be used to better understand brain and behavior.

Network analysis for modeling complex systems in SLA research

“…However, this discussion has been mostly theoretical, with very few empirical studies using social network analysis from a CDST perspective. For example, although some applied linguistics researchers have used social network analysis to map the distribution of conversational topics of bilinguals in different contexts (Tiv et al, 2020) and to assess the impact of social networks in study abroad contexts (Gautier, 2019;Paradowski et al, 2021;Zappa-Hollman & Duff, 2014), these studies are not typically informed by CDST.…”

What Can Network Science Tell Us About Phonology and Language Processing?

“…In the case of phonological networks, the phoneme inventory, canonical syllable structures, and phonotactic constraints (e.g., Vitevitch & Luce, 2016) of a given language may all impose costs that constrain the attachment of a new node in the network. The presence of more than one language in the lexicon (i.e., being bi‐ or multi‐lingual) may also impose a cost that constrains the attachment of a new node in the network (e.g., Bilson, Yoshida, Tran, Woods, & Hills, 2015; Tiv, Gullifer, Feng, & Titone, 2020). Alternatively, growth algorithms other than preferential attachment may influence the acquisition of words in the phonological network and may result in a degree distribution in the phonological networks that deviates from a power law (e.g., Hills, Maouene, Maouene, Sheya, & Smith, 2009; Siew & Vitevitch, 2020a,2020b).…”

Bilingual language experience as a multidimensional spectrum: Associations with objective and subjective language proficiency

“…Instead, it exists on a multidimensional spectrum, and the social factors surrounding this experience have consequences for language processing, cognitive processing, and brain organization (e.g., Anderson, Hawrylewicz, et al, 2018;Baum & Titone, 2014;Dash, Berroir, Joanette, & Ansaldo, 2019;DeLuca et al, 2019;DeLuca, Rothman, Bialystok, & Pliatsikas, 2020;Gullifer & Titone, 2019;Li, Legault, & Litcofsky, 2014;Luk & Bialystok, 2013;Sulpizio et al, 2019;Tiv, Gullifer, Feng, & Titone, 2020). Given that these phenomena are studied in different locations around the world, a common quantification of these experiences is necessary to ensure comparability between studies.…”