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English and French Online Comments

A Text Linguistic Comparison of Popular Science Magazines

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John Marcus Sommer

In recent years, text and media linguistics have focused on genres in the new media. This is almost always accompanied by the question of the establishment and development of such content. Due to the diversity of genres and their dynamic development one can speak of an almost inexhaustible field of research. The book is located in this field of research. Its goal is to examine the origin and nature of readers’ comments by readers of French and English popular science magazines. Media content is dissected by using text linguistic tools. Transmedial cultures are explored across time, platforms, languages, and editing houses.

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14. Quantitative Analysis

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For the quantitative Analysis, the comments in the 2015 corpus were coded partly in MAXQDA and partly by using an observation sheet in excel. MAXQDA33 is a qualitative data analysis software that was used to organize the screenshots of the websites used for the corpus as well as for coding. After coding, MAXQDA automatically counts the occurrence of the coded sequences in the different sub-corpora and extracts them as Excel sheets. This was done for the coding of the micro-linguistic features Comments, Share, Hashtag, Emoticon, Quote Article, and Links. The codes Conflict, Complete Support, Turns, and Participants were counted manually. After the coding procedure, the data sheets were imported in the statistical analysis program SPSS. The main hypothesis behind the quantitative analysis is that the frequency of the codes differs according to the independent variables language, platform, and magazine.

For the language comparison, an independent two samples t-test was conducted (Bittrich & Blankenberger 2011: 119) to compare the quantity of the codes Complete Support, Conflict, Turns, and Participants in the English and French sub-corpora. The results are shown in Fig. 14 below.



Fig. 14: Comparison Conversations English/French

There were no significant differences in the scores for complete supports between English (M=0.29; SD=0.46) and French (M=0.25; SD=0.43) (t(.95; 406)=1.77; p=.76). Accordingly, in the analyzed corpus language does not seem to influence the occurrence of conversations in which participants completely support each other.

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