HU Credits: 2

Degree/Cycle: 2nd degree (Master)

Responsible Department: Communication & Journalism

Semester: 2nd Semester

Teaching Languages: English

Campus: Mt. Scopus

Course/Module Coordinator: Christian Baden

Coordinator Email: c.baden@mail.huji.ac.il

Coordinator Office Hours: Wednesday 16-18

Teaching Staff:
Dr. Christian Baden

Course/Module description:
In our contemporary world, virtually every part of life involves digital texts: Whether we read for our studies or just a cookbook, write work emails, use entertainment media or communicate with our friends, and even when we order stuff online or leave that scathing review on yelp, texts contain immense amounts of information that may be of interest for social scientific research. In this class, we learn how to recover that information from large bodies of text, using quantitative textual analysis. Following some foundational considerations about quantitative content analysis, we address a variety of current developments and challenges in quantitative text analysis. We practice different techniques, including both manual and computational analysis, and see how different strategies can be used to extract different information from digital texts. The class includes an introduction to text mining and semantic network analysis, and discusses different ways in which qualitative and quantitative strategies can be combined to improve our ability to glean more information from texts.

Course/Module aims:
The class aims to build a firm understanding of the procedures, challenges and capabilities of quantitative text analysis; to discuss important developments in the analysis of digital texts of different kinds (from document archives and news discourse to interactive communication and the variety of texts generated through our daily use of digital media); to enable participants to understand and evaluate existing research done in content analysis, text mining and semantic network analysis; to practice the development of sound strategies for textual analysis and their application in both academic and professional contexts.

Learning outcomes - On successful completion of this module, students should be able to:
- Understand the principles & procedures, quality criteria and validation strategies needed in quantitative textual research
- Distinguish different kinds of analytic options, their respective strengths and weaknesses and key applications
- Contextualize insights gained from text-analytic research, including contemporary approaches to text mining, to gain a founded understanding of its implications and limitations
- Critically evaluate present developments in quantitative text analysis and its underlying challenges
- Perform high-quality content-analytic research, and be able to employ basic software tools for computational analysis to aid the investigation
- Design and validate suitable research strategies for quantitative textual research

Attendance requirements(%):
85

Teaching arrangement and method of instruction:

Course/Module Content:
11.03.19 Getting to terms: Why quantify textual contents?
18.03.19 What's in a word: The classification of meaning
25.03.19 To code or not to code? The making of intersubjective criteria
01.04.19 All that can go wrong…: Challenges in manual content analysis
08.04.19 Say it exactly: Rule-based approaches to computer-assisted text analysis
15.04.19 Something like this: Semi-supervised approaches & machine classification
29.04.19 Probably meaningful: Unsupervised approaches & topic modeling
06.05.19 Bringing context back in: Network Text Analysis
13.05.19 Beyond the text: Finding frames and other latent meaning
20.05.19 Bring your own problem: Challenges in text analysis
27.05.19 [project work: no class]
10.06.19 Methods on the edge: Hybrid and Dynamic approaches
17.06.19 Presentation of measurement strategies
24.06.19 The future of quantitative text analysis: Challenges & Agendas

Required Reading:
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage. Chapter 2 (pp. 18-43)
Mourao, R. R., & Robertson, C. (2019). Fake news as discursive integration: An analysis of sites that publish false, misleading, hyperpartisan and sensational information. Journalism Studies.
Baden, C. & Sharon. T. (2019). Blinded by the Lies? Toward an operational strategy for delineating conspiracy theories in interactive discourse. Paper presented at the 69th ICA Annual Conference, Washington, D.C.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage. Chapter 7 (pp. 125-149)
Boxman-Shabtai, L. & Shifman, L. (2013). When ethnic humor goes digital. New Media & Society, 17(4), 520-539.
Skalski, P. D., Neuendorf, K. A. & Cajigas, J. A. (2017). Content analysis in the interactive media age. In K. A. Neuendorf (Ed.), The content analysis guidebook (pp. 201-242). Thousand Oaks, CA: Sage.
Muddiman, A., & Stroud, N. J. (2017). News values, cognitive biases, and partisan incivility in comment sections. Journal of Communication, 67, 5866-5609.
van Cuilenburg, J. J., Kleinnijenhuis, J., & de Ridder, J. A. (1988). Artificial intelligence and content analysis: Problems of and strategies for computer text analysis. Quality & Quantity, 22, 65-97.
Burscher, B., Vliegenthart, R. & de Vreese, C. H. (2015). Using supervised machine learning to code policy issues: Can classifiers generalize across contexts? The Annals of the American Academy of Political and Social Science, 659(1), 122-131.
Hirst, G., Riabinin, Y., Graham, J., Boizot-Roche, M., & Morris, C. (2014). Text to ideology or text to party status? In B. Kaal, I. Maks, & A. van Elfrinkhof (Eds.), From text to political positions: Text analysis across disciplines (pp. 93-115). Amsterdam, Netherlands: John Benjamins.
Günther, E., & Quandt, T. (2016). Word counts and topic models. Digital Journalism, 4(1), 75-88.
Jacobi, C., van Atteveldt, W., & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism, 4(14), 89-106.
Doerfel, M. L. (1998). What constitutes semantic network analysis? A comparison of research and methodologies. Connections, 21(2), 16-26.
Kleinnjienhuis, J. & van Atteveldt, W. (2014). Positions of parties and political cleavages between parties in texts. In B. Kaal, I. Maks, & A. van Elfrinkhof (Eds.), From text to political positions: Tekst analysis across disciplines (pp. 1-20). Amsterdam, the Netherlands: John Benjamins.
Matthes, J., & Kohring, M. (2008). The content analysis of media frames: Toward improving reliability and validity. Journal of Communication, 58, 258-279.
Papacharissi, Z., & de Fatima Oliveira, M. (2008). News frames terrorism: A comparative analysis of frames employed in terrorism coverage in U.S. and U.K. newspapers. The International Journal of Press/Politics, 13, 52-74.
Boumans, J. W., & Trilling, D. (2016). Taking stock of the toolkit. Digital Journalism, 4(1), 8-23.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage. Chapter 14 (pp. 339-364)
Baden, C., Kligler-Vilenchik, N. & Yarchi, M. (2019). Hybrid Content Analysis: Toward a strategy for the computer-assisted classification of large text corpora using topic modeling. Paper presented at the 69th ICA Annual Conference, Washington, D.C.
Nicholls, T. & Bright, J. (2018). Understanding news story chains using information retrieval and network clustering techniques. Communication Methods & Measures.

Additional Reading Material:

Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 0 %
Assignments 40 %
Reports 0 %
Research project 40 %
Quizzes 0 %
Other 20 %
Active Participation

Additional information: