Challenges of building a CMC corpus for analyzing writer's style by age: The DiDi project

Abstract

This paper introduces the project DiDi in which we collect and analyze German data of computer-mediated communication (CMC) written by internet users from the Italian province of Bolzano – South Tyrol. The project focuses on quasi-public and private messages posted on Facebook, and analyses how L1 German speakers in South Tyrol use different varieties of German (e.g. South Tyrolean Dialect vs Standard German) and other languages (esp. Italian) to communicate on social network sites. A particular interest of the study is the writers’ age. We assume that users of different age groups can be distinguished by their linguistic behavior. Our comprehension of age is based on two conceptions: a person’s regular numerical age and her/his digital age, i.e. the number of years a person is actively involved in using new media. The paper describes the project as well as its diverse challenges and problems of data collection and corpus building. Finally, we will also discuss possible ways of how these challenges can be met. 1 Language in computer-mediated communication There is a wealth of studies in the corpus linguistic literature on the particularities of language used in computer-mediated communication (CMC) (e.g. for German Bader 2002, Demuth and Schulz 2010, Durscheid et al. 2010, Gunthner and Schmidt 2002, Harvelid 2007, Kessler 2008, Kleinberger Gunther and Spiegel 2006, Siebenhaar 2006, Siever 2005, Salomonsson 2011). Especially, the use of “netspeak” phenomena (Crystal 2001) such as emoticons, acronyms and abbreviations, interaction words, iteration of letters, etc. have attracted attention. The studies describe different functions of such phenomena within CMC. Features transferred from spoken language, such as discourse particles, vernacular and dialectal expressions are frequently mentioned characteristics of CMC. They serve to transmit informality of a given message, comment, or status post. Writers often use emoticons, interaction words (e.g. *grin*), abbreviations (e.g. lol), and spelling changes such as the iteration of letters (e.g. coooooll) to compensate for the absence of facial expressions, gestures and other kinesic features, and prosody. Many emoticons, interaction words, and abbreviations are “verbal glosses” for performed actions and aspects of specific situations. In addition, there are also particularities in spelling that people use without the aim of representing features of spoken language and that deviate from the standard variety. To cover such phenomena (e.g. n8 for ‘night’), we will follow Androutsopoulos (2007; 2011) and use the term “graphostylistics”. Finally, all forms of shortening (e.g. lol, n8, and thx for thanks) are often used for economic reasons to perform speedy conversations in chats and instant messages. The use of shortenings can also be motivated due to character restrictions of the used services. Differences between the use of language in CMC and in traditional written genres were often described with respect to the model of Koch/Oesterreicher (1985; 2008). The model