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A large database is a desirable basis for multimodal analysis. The development of more elaborate methods, data banks, and tools for a stronger empirical grounding of multimodal analysis is a prevailing topic within multimodality. Prereq- uisite for this are corpora for multimodal data. Our contribution aims at developing a proposal for gathering and building multimodal corpora of audio-visual social media data, predominantly YouTube data.Our contribution has two parts: First we outline a participation framework which is able to represent the complexity of YouTube communication. To this end we ‘dissect’ the different communicative and multimodal layers YouTube consists of. Besides the Video performance YouTube also integrates comments, social media operators, commercials, and announcements for further YouTube Videos. The data consists of various media and modes and is interactively engaged in various discourses. Hence, it is rather difficult to decide what can be considered as a basic communicative unit (or a ‘turn’) and how it can be mapped. Another decision to be made is which elements are of higher priority than others, thus have to be integrated in an adequate transcription format. We illustrate our conceptual considerations on the example of so-called L e t’s Plays, which are supposed to present and comment Computer gaming processes.The second part is devoted to corpus building. Most previous studies either worked with ad hoc data samples or outlined data mining and data sampling strategies. Our main aim is to delineate in a systematic way and based on the conceptual outline in the first part necessary elements which should be part of a YouTube corpus. To this end we describe in a first Step which components (e.g., the Video itself, the comments, the metadata, etc.) should be captured. ln a second Step we outline why and which relations (e.g., screen appearances, hypertextual struc- tures, etc.) are worth to get part of the corpus. In sum, our contribution aims at outlining a proposal for gathering and systematizing multimodal data, specifically audio-visual social media data, in a corpus derived from a conceptual modeling of important communicative processes of the research object itself.
In so-called Let’s Plays, video gaming is presented and verbally commented by Let’s Players on the internet for an audience. When only watched but not played, the most attractive features of video games, immersion and interactivity, get lost – at least for the internet audience. We assume that the accompanying reactions (transmitted via a so-called facecam) and verbal comments of Let’s Players on their game for an audience contribute to an embodiment of their avatars which makes watching a video game more attractive. Following an ethnomethodological conversation analytical (EMCA) approach, our paper focusses on two practices of embodying avatars. A first practice is that Let’s Players verbally formulate their actions in the game. By that, they make their experiences and the 'actions' of avatars more transparent. Secondly, they produce response cries (Goffman) in reaction to game events. By that, they enhance the liveliness of their avatars. Both practices contribute to a co-construction of a specific kind of (tele-)presence.