People are gathering in social media platforms in order to connect, represent, debate and purchase. Accordingly, data sourced from these platforms can and is widely used to create knowledge on contemporary social interaction, practice and culture. In this seminar, students are introduced to critical approaches to social media analysis using and experimenting with various methods emanating from qualitative social sciences and data sciences. The thematic focus of analysis lies on the characterization and evaluation of debates concerning scientific issues including the climate crisis, COVID-19 and associated conspiracy theories on YouTube and Twitter. Throughout the course of the seminar, experts from these scientific domains will be invited to suggest and discuss possible entry points and topics to be further investigated by the students.

While students will experiment with various tools for data extraction, visualization and analysis, the main objective of the seminar is to enable a critical evaluation of methods and their contribution to knowledge creation concerning digitally-mediated social interaction. This includes the entanglement of approaches such as Grounded Theory (qualitative coding), digital ethnography and machine learning. In particular, data science methods promise new investigative opportunities and a scalability to larger datasets, which are common in the analysis of social media data. Students will learn how to make data science methods productive, while at the same time grounding their investigation in empirically-observable social practice by use of qualitative methods. To do so, students will be introduced to human-centered research approaches pushed forward by the HCC Research Group at FU.

Allgaier, Joachim. „Science and Environmental Communication on YouTube: Strategically Distorted Communications in Online Videos on Climate Change and Climate Engineering“. Frontiers in Communication 4 (2019). https://doi.org/10.3389/fcomm.2019.00036.

Baumer, Eric P. S., David Mimno, Shion Guha, Emily Quan, und Geri K. Gay. „Comparing Grounded Theory and Topic Modeling: Extreme Divergence or Unlikely Convergence?“ Journal of the Association for Information Science and Technology 68, Nr. 6 (Juni 2017): 1397–1410. https://doi.org/10.1002/asi.23786.

Chen, Nan-Chen, Margaret Drouhard, Rafal Kocielnik, Jina Suh, und Cecilia R Aragon. „Using Machine Learning to Support Qualitative Coding in Social Science: Shifting The Focus to Ambiguity“. ACM Transactions on Interactive Intelligent Systems 9, Nr. 4 (2018): 21.

Geiger, R Stuart, und David Ribes. „Trace Ethnography: Following Coordination through Documentary Practices“. In 2011 44th Hawaii International Conference on System Sciences, 1–10. Kauai, HI: IEEE, 2011. https://doi.org/10.1109/HICSS.2011.455.

Marres, Noortje, und Carolin Gerlitz. „Interface Methods: Renegotiating relations between digital social research, STS and sociology“. Sociological Review, 2015. http://research.gold.ac.uk/11343/.

Muller, Michael, Shion Guha, Eric P.S. Baumer, David Mimno, und N. Sadat Shami. „Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination“. In Proceedings of the 19th International Conference on Supporting Group Work - GROUP ’16, 3–8. Sanibel Island, Florida, USA: ACM Press, 2016. https://doi.org/10.1145/2957276.2957280.

Pfeffer, Jürgen, Katja Mayer, und Fred Morstatter. „Tampering with Twitter’s Sample API“. EPJ Data Science 7, Nr. 1 (Dezember 2018): 1–21. https://doi.org/10.1140/epjds/s13688-018-0178-0.

Schäfer, Mike S. „Online Communication on Climate Change and Climate Politics: A Literature Review“. WIREs Climate Change 3, Nr. 6 (2012): 527–43. https://doi.org/10.1002/wcc.191.

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