Open Access

Power, in this article, is to be understood as an instrument of force that is imposed purposely in order to influence, affect or persuade others. The question here is whether such power is due to aggressive expressions (lexical level) or to context-dependent aspects (discourse level) that become relevant when insulting persons via new media. I will distinguish between “cyberbullying” as an attempt to hurt a persons feelings directly via personal SMS or email and “virtual character assassination attempts” that include third parties as an audience. Potential readers not directly involved are considered a constitutive eliciting element of power. It is assumed that their existence is even more important and effective (in terms of strengthening the perpetrators power) than aggressive language.

This study investigates the question of whether the processing of complex anaphors require more cognitive effort than the processing of NP-anaphors. Complex anaphors refer to abstract objects which are not introduced as a noun phrase and bring about the creation of a new discourse referent. This creation is called “complexation process”. We describe ERP findings which provide converging support for the assumption that the cognitive cost of this complexation process is higher than the cognitive cost of processing NP-anaphors.

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.