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MICHAŁ B. PARADOWSKI, AGNIESZKA CIERPICH–KOZIEŁ, CHIH–CHUN CHEN, JEREMI K. OCHAB MLJ Volume106, Issue4 Winter 2022 Pages 694-725 This data-driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students enrolled in an intensive German language course on second language acquisition (SLA) outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that (a) the best predictor of target language (TL) performance is reciprocal interactions in the language being acquired, (b) the proportion of output in the TL is a stronger predictor than input (Principle of Proportional Output), (c) there is a negative relationship between performance and interactions with same-first-language speakers, (d) a significantly underperforming English native-speaker dominated cluster is present, and (e) there are more intense interactions taking place between students of different proficiency levels. Unlike previous study abroad social network research concentrating on the microlevel of individual learners’ egocentric networks and presenting an emic view only, this study constitutes the first application of computational SNA to a complete learner network (sociogram). It provides new insights into the link between social relations and SLA with an etic perspective, showing how social network configuration and peer learner interaction are stronger predictors of TL performance than individual factors such as attitude or motivation, and offering a rigorous methodology for investigating the phenomenon. Read the full article at: onlinelibrary.wiley.com
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Suggested by
mohsen mosleh
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Mohsen Mosleh, Cameron Martel, Dean Eckles, David G. Rand Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.
Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho & Ronaldo Menezes
Applied Network Science volume 5, Article number: 24 (2020) Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.
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Suggested by
Michael Park
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Michael Park, Erin Leahey, Russell Funk Although the number of new scientific discoveries and technological
inventions has increased dramatically over the past century, there have also
been concerns of a slowdown in the progress of science and technology. We
analyze 25 million papers and 4 million patents across 6 decades and find that
science and technology are becoming less disruptive of existing knowledge, a
pattern that holds nearly universally across fields. We link this decline in
disruptiveness to a narrowing in the utilization of existing knowledge.
Diminishing quality of published science and changes in citation practices are
unlikely to be responsible for this trend, suggesting that this pattern
represents a fundamental shift in science and technology. Read the full article at: arxiv.org
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Suggested by
mohsen mosleh
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Mohsen Mosleh, Katelynn Kyker, Jonathan D. Cohen & David G. Rand Nature Communications volume 11, Article number: 3099 (2020) The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive) versus controlled (e.g., deliberative) cognition, interact with each other, and influence the environment (i.e., game payoffs). We find that globalized direct contact between agents can either favor or disfavor control, depending on whether controlled agents are harmed or helped by contact with automatic agents; globalized environment disfavors cognitive control, while also promoting strategic diversity and fostering mesoscale communities of more versus less controlled agents; and globalized learning destroys mesoscale communities and homogenizes the population. These results emphasize the importance of the scale of interaction for the evolution of cognition, and help shed light on modern challenges. Humankind is in a period of unprecedented cognitive sophistication as well as globalization. Here, using an evolutionary game theory model, the authors reveal ways in which the transition from local to global interaction can have both positive and potentially negative consequences for the prevalence of cognitive sophistication in the population.
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Suggested by
dvasques
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Demival Vasques Filho, Dion R. J. O'Neale Dynamical processes, such as the diffusion of knowledge, opinions, pathogens, "fake news", innovation, and others, are highly dependent on the structure of the social network on which they occur. However, questions on why most social networks present some particular structural features, namely high levels of
transitivity and degree assortativity, when compared to other types of networks remain open. First, we argue that every one-mode network can be regarded as a projection of a bipartite network, and show that this is the case using two simple examples solved with the generating functions formalism. Second, using synthetic and empirical data, we reveal how the combination of the degree distribution of both sets of nodes of the bipartite network --- together with the presence of cycles of length four and six --- explains the observed levels of transitivity and degree assortativity in the one-mode projected network. Bipartite networks with top node degrees that display a more right-skewed distribution than the bottom nodes result in highly transitive and degree assortative projections, especially if a large number of small cycles are present in the bipartite structure.
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