Course: Social Media Analysis

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Course title Social Media Analysis
Course code KZU/SASS
Organizational form of instruction Lecture + Seminar
Level of course Bachelor
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Libicher Miroslav, Mgr. Ph.D.
Course content
1) Introduction to Social Media and Social Network Analysis 2) Principles of Computer-Mediated Communication (CMC) 3) Identity Construction in Online Social Environments 4) Intertextuality and Framing 5) Social Media Data Collection Strategies 6) Computer-Mediated Discourse / Conversation Analysis 7) Discourse Analysis 8) Digital Ethnography and Online Fieldwork 9) Visual Content Analysis 10) Data Mining on Social Media Platforms 11) Corpus Linguistics and Corpus-Assisted Analysis 12) Sentiment Analysis 13) Course Review and Student Presentations

Learning activities and teaching methods
Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Group work
  • Homework for Teaching - 6 hours per semester
  • Semestral Work - 20 hours per semester
  • Attendace - 26 hours per semester
Learning outcomes
The objective of the course is to provide students with a foundational orientation in the analysis of social networks and user-generated content distributed through these platforms. Emphasis is placed primarily on the specific characteristics of communication on social networks and the analysis of their linguistic and visual elements. Attention will also be given to the ways in which news content is disseminated and recontextualized across these platforms. Course graduates will be introduced to both quantitative and qualitative approaches to social network analysis and will learn to apply methods from corpus linguistics and multimodal discourse analysis for these purposes. Additionally, they will gain an understanding of the communicative principles underlying social networks and comprehend their relation to other media types.
Based on the course description, students will acquire the following competencies: Data Analysis Proficiency: Knowledge of methodologies and possibilities for analyzing social networks and user-generated content (UGC). Linguistic and Visual Analysis: Ability to examine and evaluate specific linguistic and visual elements of social media communication. Application of Advanced Methodologies: Practical application of techniques from corpus linguistics and multimodal discourse analysis. Methodological Flexibility: Ability to apply both quantitative and qualitative research approaches. Critical Understanding of News Dissemination: Understanding of the mechanisms through which news content is disseminated and recontextualized across social platforms. Understanding of the Media Ecosystem: Comprehension of the communicative principles underlying social networks and their interconnection with other media types
Prerequisites
Students are expected to possess basic computer literacy and reading proficiency in English-language academic materials.

Assessment methods and criteria
Student performance, Dialog, Seminar Work

Colloquium, seminar paper
Recommended literature
  • Page, R., Barton, D., Lee, C., Unger, J. W., & Zappavigna, M. (2022). Researching language and social media: A student guide.
  • Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital ethnography: Principles and practice.
  • Quan-Haase, A., & Sloan, L. (Eds.). (2022). The SAGE handbook of social media research methods.
  • Salganik, M. J. (2018). Bit by bit: Social research in the digital age.
  • Zappavigna, M. Discourse of Twitter and social media: How we use language to create affiliation on the web.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Arts Study plan (Version): Data Analyst in Social Sciences (2026) Category: Social sciences 2 Recommended year of study:2, Recommended semester: Summer
Faculty: Faculty of Arts Study plan (Version): Data Analyst in Social Sciences (2023) Category: Social sciences 2 Recommended year of study:2, Recommended semester: Summer