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In recent years, interest in modeling and analyzing psychological phenomena, such as language and lexical memory, with the tools of network science has been on the rise and a considerable body of research in this area has been accumulated. Network science was developed to measure and represent statistical dependencies between connected entities and provides a powerful computational approach to quantify dyadic relationships. A network is made up of nodes, which represent the basic unit of the system and links, or edges, which signify the relations between them. Linguistic networks can be based on various concepts, for instance phonological word forms, semantics, or social partners involved in communication. This class examines the relevance of network science for the study of language on various levels of analysis. We will review efforts to construct different types of language networks, characterize properties of those networks, and apply statistical analyses to elucidate the structure and complex relationships of entities within the networks.<br>
In the summer semester of 2025, this course will be offered as a project-based course. This means that it will be a mix of regular classes, online classes, and empirical projects conducted by the participants. <br> Last update: Luef Eva Maria, doc. Dr. phil., Mag. phil. (15.01.2025)
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Barabási, A. L. (2016). Network science. Cambridge, UK: Cambridge University Press. Borgatti, S. P. & Halgin, D. S. (2011). Analyzing affiliation networks. In: The SAGE handbook of social network analysis. DOI: 10.4135/9781446294413.n28 Brown, V. (2021). An introduction to linear mixed-effects modeling in R. Advances in Methods and Practices in the Psychological Science, 4/1, 1-19. Castro, N., & Siew, C. S. Q. (2020). Contributions of modern network science to the cognitive sciences: Revisiting research spirals of representation and process. Proceedings of the Royal Society A, 476/ 2238, https://doi.org/10.1098/rspa.2019.0825 De Deyne, S., Kenett, Y. N., Anaki, D., Faust, M., & Navarro, D. (2017). Large-scale network representations of semantics in the mental lexicon. In M. N. Jones (Ed.), Frontiers of cognitive psychology. Big data in cognitive science (p. 174–202). Routledge/Taylor & Francis Group. Hills, T., Maouene, J., Sheya, A., Maouene, M., & Smith, L. (2009). Longitudinal analysis of early semantic networks: Preferential attachment or preferential acquisition? Psychological Science, 20, 729-739. Kang, G. J., et al. (2017). Semantic network analysis of vaccine sentiment in online social media. Vaccine, 35, 3621-3638. Luef, E. M. (2022). Growth algorithms in the phonological networks of second language learners. Journal of Experimental Psychology: General, Advance Online, https://doi.org/10.1037/xge0001248. Luef, E. M., Resnik, P., & Gráf, T. Diffusion of phonetic learning within phonological neighborhoods. Mak, M. H. C. & Twitchell, H. (2020). Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning. Psychonomic Bulletin & Review, 27, 1059-1069. Milroy, L. (2004). Social networks. In: K. Chambers, et al. (Eds.), The handbook of language variation and change (pp. 549-572). London: Blackwell Publishing. Milroy, J., & Milroy, L. (1985). Linguistic change, social network and speaker innovation. Journal of Linguistics, 21, 339-384. Noble, B. & Fernandez, R. (2015). Centre stage: How social network position shapes linguistic coordination. Proceedings of CMCL, 29-38. Rienties, B., Héliot, Y.F., Jindal-Snape, D. (2013). Understanding social learning relations of international students in a large classroom using social network analysis. Higher Education, 66, 489–504. Siew, C. S. Q., & Vitevitch, M. (2020). An investigation of network growth principles in the phonological language network. Journal of Experimental Psychology: General, 149/12, 2376-2394. Stella, M., Beckage, N. M., & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific Reports, 7, 46730. Vitevitch, M. S., Goldstein, R., Siew, C., & Castro, N. (2014). Using complex networks to understand the mental lexicon. Yearbook of the Poznan Linguistic Meeting, 1, 119-138. Vitevitch, M. S. & Sommers, M. S. (2003). The facilitative influence of phonological similarity and neighbourhood frequency in speech production in younger and older adults. Memory & Cognition, 31, 491-504. Yucel, M., Sjobeck, G. R., Glass, R. & Rottman, J. (2020). Being in the know: Social network analysis of gossip and friendship on college campuses. DOI: 10.31234/osf.io/q8m7u. Last update: Luef Eva Maria, doc. Dr. phil., Mag. phil. (22.09.2022)
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- Attendance and participation: The success of this course relies on your participation. Whether you are working in group activities, individual workshops, or class discussions (on- or offline), you must be present and active. - Small groups will conduct projects and present them at the end of the semester
GRADING Attendance, active participation, assignments = 30% Project = 30% Oral presentation = 40% Link to the class Moodle : https://dl1.cuni.cz/course/view.php?id=8741 Last update: Luef Eva Maria, doc. Dr. phil., Mag. phil. (21.01.2025)
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Last update: Luef Eva Maria, doc. Dr. phil., Mag. phil. (21.01.2025)
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