1980’s and early 1990’s: attempts to classify CMD in two modalities of language : speech & writing, as if it were a single homogenous genre or communication type.
Popularization of Internet: the focus was on describing the liguistic features of individual genres of CMD. But the concept of “genre” can be applied to communication at different levels of specificity and is imprecise.
Modes approach (technologically-defined subtypes). But CMD was sensitive to situational factors as well.
Not clear how genre and mode approaches could be used to classify new forms of CMD or discourse that takes place via customized systems that operate within restricted (educational, governmental, etc.) domains.
Need of a classification of CMD based on multiple categories or “facets” :
- Intended as a faceted lens through which to view CMD data in order to facilitate linguistic analysis.
- Intended to complement genre or mode- based analysis.
Discourse analysts have traditionally classified discourse into types according to various criteria: modality, number of discourse participants, text type and genre or register, being in a non-exclusive and hierarchical relatioship to one another.
But classification facilitates analysis : exemplars of the same type of discourse tend to share features that distinguish them collectively from other discourse types; it facilitates comparison across types.
Classification of CMD according to pre-defined set of categories.
Bayn identified 5 factors that condition variation in CMD: the external contexts in which CMC use is situated, the temporal structure of the group, the computer system infrastructure, the purpose of communication, and the characteristics of the group and its members.
It is grounded in empirical observations
It takes the contributions of the computer system into account
Utility demonstrated through application to data
Only 5 factors
In none of the studies was classification the primary objective.
The inclusion of a set of technological factors in the approach does not assume that the computer medium exercises a determining influence on communication in all cases, although each factor has been observed to affect communication in at least some instances.
One reason for including medium factors as a separate list is to attempt to discover under what circumstances specific system features affect communication and in what ways.
It determines the order in which messages appear, what information is appended automatically to each and how it is visually presented, and what happens when the viewing window becomes filled with messages (chat, forums vs. blogs vs. wikis.)
It’s a game-like online learning environment for children 9-12 years old, used mainly in the USA, Australia and Syngapore, under the supervision of their classroom teachers, and maintained by a fictional Atlantian girl, Alim
S1. Group size (the potential audience of each blog) varies widely as a consequence of the public/private nature; rate of participaion is slower on the QA blog; and posting rights are asymmetrical (only “Alim” can post entries; in the LJ only the blog owner can post in her own blog, but commenters all have their own blogs, so everyone has a chance to both post and comment).
S2. Age, roles, previous experience and the relationship among participants differ between the two samples
S3, S5, S7. The purpose, its topic/theme, the tone of messages and comments, and the norms of interaction and norms of language use are different.
As the Internet expands, it continues to create new varieties of discourse that call out for analysis and classification.
The faceted classification scheme is intended to complement existing mode-based classification of CMD.
The faceted classification scheme classifies discourse samples in terms of clusters of variable dimensions, thereby preserving their complexity and allowing for focused comparisons within and across samples.