Open Educational Resources

6. Open Educational Resources#

The Digital Discourse Lab develops Open Educational Resources to make corpus linguistic methods more accessible to researchers working with language data in fields such as public health, social work, organizational communication, and journalism.

On this page, you can find exemplary research scenarios as well as educational videos that explain key concepts and tools related to corpus linguistics and discourse analysis.

6.1. Examples of Use#

6.1.1. Climate Change#

Tools used: Semantic Space, Context of Words, Distribution of Words

Use case: Susanne, an environmental scientist is researching climate change — a widely covered topic — but she has no experience collecting large text datasets or crawling news websites. She needs a tool to help her quickly navigate the Swiss media landscape and identify relevant terms and trends for further analysis.

Research Questions

Susanne would like to know:

What terms are most commonly used when reporting on climate change in Switzerland?
How do different media outlets write about this topic?

Step 1: Exploring Keywords with Semantic Space

To discover commonly used terms related to climate change, Susanne uses the Swiss-AL module Semantic Space. She searches for “Klimawandel” (climate change) in the German journalistic corpus, which includes high-reach German-language media published since 2010.

Semantic Space

The tool suggests semantically similar words like:

Klimaerwärmung (climate warming)
Klimaveränderung (climate change)
Erderwärmung (global warming)

It also shows contextually related terms such as:

Klimakrise (climate crisis)
Klimakatastrophe (climate catastrophe)
Artensterben (species extinction)

This gives Susanne a richer vocabulary and inspiration for deeper analysis.

Step 2: Investigating Usage with Words in Context

Next, Susanne uses the Context of Words tool to examine how the identified terms appear in articles. She opens Advanced Search and enters the following CQP query:

[lemma="Klimawandel|Klimaerwärmung|Klimaveränderung"]

This searches for all word forms of Klimawandel, Klimaerwärmung, and Klimaveränderung in the corpus.

She can now explore:

Context of Words view: showing text snippets around the search words
Distribution in Documents: showing the distribution of search words in the documents

Each example is linked to the original article in the SMD Swiss Media Database, allowing her to access full context when needed.

Context of Words view

Distribution in Documents view

Step 3: Comparing Media with Word Distribution

To understand how frequently these terms appear across different media outlets, Susanne uses the Word Distribution tool.

This allows her to:

Compare relative frequencies by source
Identify which outlets focus most on climate-related vocabulary she selected

Now that she has a clearer overview, she creates subcorpora for selected media (e.g., SWII, WOZ and WEWO) to perform deeper comparative analysis.

Distribution by Source

6.1.2. No Billag#

Tools used: Context of Words

Use case A media scholar wants to better understand how the current discussion in journalistic media about the Halbierungsinitiative (‘Halving Initiative’) functions. Therefore, he looks back at how the topic of No Billag was reported on at the time. His hypothesis is that references, for example, to democracy, neutrality, and savings were already used as argumentative strategies back then.

Research Question

How do democracy, neutrality, and costs appear in connection with No Billag?

To investigate this, the media scholar performs a query in the German Journalistic Corpus. He searches in Context of Words for No Billag and No-Billag.

Using Context of Words, he gets an initial overview of source, title, date, and context. From this overview, it can already be seen that No Billag is by no means always the main topic of an article.

To find out whether the articles primarily deal with No Billag, he switches to Distribution in Documents. Using this tool, he can identify which articles not only contain No Billag but actually deal with it in substance. This becomes clear, for example, in the comparison between No. 62 (title “Steuerreform: Es soll schnell gehen und ohne Referendum” ‘Tax reform: It should be done quickly and without a referendum’) and No. 3 (title “Wendepunkt in der Geschichte der SRG” ‘Turning point in SRG’s history’). While “No Billag” is only mentioned in No. 62, it is the main topic in No. 3.

KWICs

To answer the research question, the media scholar then selects the articles that are relevant to him (e.g., No. 3, 61, 36, 76). To read them in full, they can click on the source URL. The researcher is then redirected to the article on swissdox.ch (this requires corresponding access to swissdox.ch).

no_billag

6.2. Educational Videos#

6.2.1. Topic Modeling#

6.2.2. Keywords#

6.2.3. Frequency and Distribution#

6.3. Theory on Discourse Analysis (in German)#

6.3.1. Grounded Theory#

6.3.2. Theoretische Grundlagen der Modellierung#

6.3.3. Prinzipien der Diskursanalyse#

6.4. ZHAW Learning Modules: Discourse Analysis with Corpora (in German)#

This section presents online learning modules developed for students in Applied Linguistics at ZHAW. The project was conceptualized and led by Cerstin Mahlow in 2023. The team members include Maren Runte, Julia Krasselt, and Philipp Dreesen.