Update summaries-diedenhofen.md

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@@ -79,3 +79,6 @@ Selection is the process of choosing which data to include or exclude from your
 Based on these 5 steps, we should then document the network we created a few weeks ago with the aim of enabling someone else to obtain the same data later on.
 
 In a further step, there is also the term FAIR Data. This is a concept that states that research data must be easily findable, accessible, interoperable and reusable. The data must therefore not disappear (F), must be accessible (A), it must be possible to connect the data with another data set (I) and the data must generally be reusable for interconnectivity with other data sets (R).
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+## Scalable Reading and Voyant Tools, 04/12/2024
+As part of course on distant reading, our assignment was exercise number 3. This exercise involved transferring the comments from a YouTube video titles “Luxembourg: Poverty in Europe’s wealthiest country” into the Voyant Tools interface. The work was divided into three main parts, each exploring different features and possibilities offered by this tool. The first part of the exercise required us to explore three specific features of the interface: a word-cirrus, the list of terms, and links. First, the word-cirrus tool projects the most common words as a word cloud, with size and colour varying based on frequency. We could also adjust the number of words displayed to better visualize trends. Second, using the list of terms, it was possible to count how many times a specific word appeared in the comments, with positive terms, like “rich”, highlighted in green and negative terms, like “poor”, in red. Third, the links feature highlighted a network of the most frequent words, distinguishing primary terms in blue and their associated terms in orange, with the same “adjustment” feature possible. For instance, the word “people” (in blue) was linked to terms like “government” or “Luxembourg” (in orange). These features allowed us to visualize the vague opinions expressed in the comments and understand the general direction of the video’s main subject. In the second part, we explored two other features of Voyant Tools: contexts and collocations. First, the context tool displayed the sentences or passages where a specific was used, enabling us for example, to distinguish positive comments from negative ones and analyse options in more detail. Second, the collocation tool showed how often certain words appeared together in the same context. For example, the words “poverty” and “Europe” appeared together nine times in the comments, highlighting a significant connection between these two words. These analyses helped us understand the frequent and less-frequent relationships between terms and identify recurring themes in the comments. Finally, in the third part, we reflected on potential applications of this interface in other domains. We proposed three ideas. First, for archival research, this interface could be used to analyse historical archives. For instance, one could identify how often a specific subject, such as the Luxemburgish communist diplomat “René Blum”, is mentioned in a corpus of archives and examine the contexts in which it is referenced, like “communism” or the “Soviet Union”. Second, Voyant Tools could be employed to analyse newspaper articles, particularly the most used words used by a specific journalist or author in a publication, to conduct critical discourse analysis. Moreover, this critical discourse analysis can also be applied to topics like World War II, the Cold War, or the Crusades, enabling the user to analyse how opposed parties describes such events. Third, this tool could track the evolution of an author’s work overtime. For instance, it would be interesting to analyse how a writer’s style or themes, that lived in Germany, changed before, during, and after World War II. This exercise thus allowed us to explore the many capabilities of Voyant Tools in text analysis and to consider its practical applications in various contexts.