Sprache im 20. Jahrhundert. Gegenwartssprache
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Content analysis provides a useful and multifaceted, methodological framework for Twitter analysis. CAQDAS tools support the structuring of textual data by enabling categorising and coding. Depending on the research objective, it may be appropriate to choose a mixed-methods approach that combines quantitative and qualitative elements of analysis and plays out their respective advantages to the greatest possible extent while minimising their shortcomings. In this chapter, we will discuss CAQDAS speech act analysis of tweets as an example of software-assisted content analysis. We start with some elementary thoughts on the challenges of the collection and evaluation of Twitter data before we give a brief description of the potentials and limitations of using the software QDA Miner (as one typical example for possible analysis programmes). Our focus will lie on analytical features that can be particularly helpful in speech act analysis of tweets.
This paper aims at showing how quantitative corpus linguistic analysis can inform qualitative analysis of digital media discourse with respect to the mediality of language in use. Using the example of protest discourse in Twitter, in the field of anti-Islamic ‘Pegida’ demonstrations, a three-step method of collecting, reducing and interpreting salient data is proposed. Each step is aligned with operative medial features of the microblog: hashtags, retweets and @-interactions. The exemplary analysis reveals the importance of discussions of attendance numbers in protest discourse and the asymmetry between administrative (i.e. the police) and non-administrative discourse agents. Furthermore, it exemplifies how frequency analysis and sequence analysis can be combined for research in media linguistics.
This paper explores on the basis of empirical research, how patterns of interaction and argumentation in political discourse on Twitter evolve as translocal communities in the creative shape of “joint digital storytelling”. Joint storytelling embraces coordinated activities by multiple actors focusing on a shared topic. By adding personal information and evaluation, participants construct an open narrative format, which can be inviting and inspiring for others, who then join in with their own narratives. This model will be exemplified by analyzing a large amount of tweets (107,000) collected during a political conflict between proponents and adversaries of a local traffic project in Germany. Analysis is based on (1) the textual level, (2) the operative level (hashtags, @- and RT-Symbol, hyperlinks etc.) and (3) the visual level of storytelling (embedded photos, videos). Results show a new way of creating translocal online communities and political deliberation.