Tag: dynamic networks

Peer interaction dynamics and L2 learning trajectories during study abroad: A longitudinal investigation using dynamic computational Social Network Analysis

Paradowski, M.B., Whitby, N., Czuba, M. & Bródka, P. (2024). Language Learning. DOI: 10.1111/lang.12681

This is the first application in second language acquisition of quantitative Social Network Analysis reconstructing a complete learner network with repeated (three) measurement points. Apart from the empirical contribution showcasing exciting findings from an intensive study-abroad Arabic program, the text can also serve as a primer of centrality metrics, providing in-depth explanation of the most commonly used centrality measures in network science – to the best of our knowledge, the first such 101 in applied linguistics. The materials, dataset, as well as code are all openly available on OSF and IRIS.

Abstract

Using computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3-month intensive study-abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same-gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self-perceived progress across skills; and the interplay of context and gender.

Read the full article at https://onlinelibrary.wiley.com/doi/10.1111/lang.12681

Group mixing drives inequality in face-to-face gatherings

Marcos Oliveira, Fariba Karimi, Maria Zens, Johann Schaible, Mathieu Génois & Markus Strohmaier
Communications Physics volume 5, Article number: 127 (2022)

Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in which a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens inequality. Finally, we expose group under-representation in degree rankings due to mixing dynamics and propose a way to reduce such biases. The emergence of inequality in social interactions can depend on a number of factors, among which the intrinsic attractiveness of individuals, but also group size the presence of pre-formed social ties. Here, the authors propose “social attractiveness” as a mechanism to account for the emergence of inequality in face-to-face social dynamics and show this reproduces real-world gathering data, predicting the existence of a critical group size for the minority group below which higher cohesion among its members leads to higher inequality.

Read the full article at: www.nature.com

A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Saad Alqithami
Computers 2021, 10(1), 12

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

Read the full article at: www.mdpi.com