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.

Keywords: bilingualism; cognitive control; conversational topics; individual differences; network analysis; network science.

Figure 1.

Distribution of participant demographics on language age of acquisition and mean self-rated proficiency (across reading, writing, speaking, and listening) for the dominant and non-dominant language.

Figure 2.

Analytic pipeline for network analysis (prior to community detection)

Figure 3.

Pseudocode of the Louvain Method taken from Aynaud & Guillaume (2010)

Figure 4.

Average network for each context. Nodes represent topics of conversation, and edges indicate whether two topics co-occurred in each context. Edges are weighted by the total number of languages used to discuss two topics in a given context, as demonstrated by color. Green and blue hues indicate more languages and pink and yellow hues indicate fewer languages. The total number of respondents for each context are indicated below the network.

Figure 5.

Mean values for weight and three network measures on each context. Error bars indicate plus or minus one standard error of the mean.

Figure 6.

Average network for each language. Nodes represent topics of conversation, and edges indicate whether two topics discussed in that language co-occurred in the same context. Edges are weighted by the total number of contexts that two topics are discussed in, as demonstrated by color. Purple hues indicate more contexts and teal hues indicate fewer contexts. The total number of respondents for each language is indicated below the network.

Figure 7.

Louvain Community Detection algorithm applied to each of the language networks. This tool groups nodes through modularity, or the probability that a node belongs to a community minus such probability if the edges were distributed at random. Two communities (C1, C2) are detected for the dominant language, and three communities (C1, C2, C3) are detected for the non-dominant language.