Language network analysis - AAA500182

• Title: Language network analysis
• Guaranteed by: Department of the English Language and ELT Methodology (21-UAJD)
• Faculty: Faculty of Arts
• Actual: from 2023
• Semester: summer
• Points: 0
• E-Credits: 5
• Examination process: summer s.:
• Hours per week, examination: summer s.:0/2, C [HT]
• Capacity: unknown / 15 (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Additional information: https://dl1.cuni.cz/course/view.php?id=8741
• Note: course can be enrolled in outside the study plan; enabled for web enrollment
• Guarantor: doc. Dr. phil. Eva Maria Luef, Mag. phil.
• Teacher(s): doc. Dr. phil. Eva Maria Luef, Mag. phil.

Annotation

Last update: doc. Dr. phil. Eva Maria Luef, Mag. phil. (03.02.2024)

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.
In the summer semester 2024 this course will be taught online using the platform Teams. Please make sure that you have a university Teams account to participate. Students can either attend the course online from room P111 - which will be reserved during course time - or join from home.

Literature

Last update: doc. Dr. phil. Eva Maria Luef, Mag. phil. (22.09.2022)

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.

Requirements to the exam

Last update: doc. Dr. phil. Eva Maria Luef, Mag. phil. (21.09.2022)

-  Attendance and participation: The success of this course relies on your participation. Whether you are working in group activities, individual workshops, or class discussions, you must be present and active.

-  Readings: I will provide required and optional literature for each lesson on Moodle.

-  There will be homework assignments for you to do throughout the semester. They are mandatory and form a crucial part of your grade.

-  Presentation: Each student will give one in-class presentation on a topic of their choice (15 min.)

  

GRADING

Attendance, active participation, reading = 20%

Assignments = 40%

Oral presentation  = 40%

Syllabus

Last update: doc. Dr. phil. Eva Maria Luef, Mag. phil. (22.09.2022)

Session	Topic	Required Readings
1	Introduction: What is a network?
2	Network properties	Barabási, 2016: chapter 1 Castro & Siew, 2020
3	Lexical networks: Phonological word forms	Vitevitch et al., 2014 Vitevitch & Sommers, 2003
4	Constructing phonological networks	Gephi, R
5	Lexical networks: Semantics	De Deyne et al., 2017 Kang et al., 2017 Mak & Twitchell, 2020
6	Constructing semantic networks	Stella et al., 2017
7	Statistical analysis of network data (regression, mixed effects models)	Brown, 2021
8	Language change from the network perspective	Luef, Resnik, & Gráf, forthcoming Milroy, 2004 Milroy & Milroy, 1985
9	Information propagation in social networks: Spreading gossip and rumors	Borgatti & Halgin, 2011 Yucel et al., 2020
10	Communication accommodation in social networks	Noble & Fernandez, 2015 Rienties et al, 2013
11	Lexical network growth	Hills et al., 2009 Luef, 2022 Siew & Vitevitch, 2020
12	Student presentations
13	Student presentations