Íslenskt mál og almenn málfræði - 01.01.2023, Qupperneq 148

updates as initiative posts and comments as responsive posts. Also, I used the
coding category linguistic repertoires and coded posts with a specific language
when they showed words, phrases, orthography etc. that could be associated with
that language. Beyond that, the category contained the code independent feature,
which I used to refer to all features that cannot be assigned to a specific language
including emojis or verbalized laughter. Also, I used the code multiple resources
to signify posts that contained more than one linguistic resource. Then I calcu-
lated proportional values and looked for relationships between linguistic reper-
toires and participatory roles as well as between linguistic repertoires and the use
of multiple resources.
Secondly, I conducted a feature or word analysis in which I identified and
counted individual features. For this purpose, I used the open-source application
Voyant Tools to detect the 50 most common unique features as well as the 50
most common content words in the corpus.
The results of my quantitative study show that the users draw on a range of
different linguistic resources including different linguistic codes, but also inde-
pendent features such as emojis and verbalized laughter. Nonetheless, Icelandic
is the most common linguistic code in the data set, followed by independent and
English features. Independent features appear in half of all posts. They are thus
a common phenomenon in the corpus. Although English is used to a much lesser
extent than Icelandic is, it is still quite prominent in the data set. This suggests a
certain importance of English as a resource. Beyond that, the users mix features
from different resources in about half of all posts. All linguistic resources appear
more often combined with other resources than on their own. Therefore, we can
say that the combination of different resources is a common linguistic strategy
among the informants. In addition, I detected some variation between initiative
and responsive posts. While Icelandic is the most common linguistic code in both
initiative and responsive posts, it is more often employed in responsive contribu-
tions, which contain more independent features and a mix of multiple resources.
An explanation for this could be that responsive posts are often directed at indi-
vidual contacts and characterized by more personalized communication that
include features such as verbalized laughter, or emojis. Initiative posts, on the
other hand, can be described as less personal and are thus characterized by prac-
tices that appeal to broader audiences.
In the feature analysis, I detected the 50 most common unique features and
content words. However, with the Voyant Tools application I could only detect
features that are based on alphabetical writing, so that I cannot make any clear
statements about the types of independent features that are prominent in the data
set. Nonetheless, the feature analysis could still give a valuable insight into the
informants’ digital practices. Firstly, the feature analysis suggests that the corpus
comprises primarily Icelandic features. Although some English words can be
found among the most common unique features, they do appear to a much lesser
Vanessa Monika Isenmann148