Susan Herring

This study investigates to what extent greetings on a social network site popular with South Asians are associated with the degree of interpersonal closeness of the users, educated
middle class Bengalis in the United States. Computer-mediated discourse analysis, face-toface interviews, and participant observation were employed to analyze the textual comments
exchanged in 79 Bengali dyads. The analyses reveal a relationship between degree of social distance and frequencies of Bangla greetings when type of greeting (regular vs. ritualistic)
and manner of expression (unmarked vs. intensified) are taken into account. However, some aspects of the interplay between interpersonal closeness and greetings can only be
understood if cultural nuances and medium factors are considered.

This article describes a classification scheme for computer-mediated discourse that classifies samples in terms of clusters of features, or " facets ". The goal of the scheme is to synthesize and articulate aspects of technical and social context that influence discourse usage in CMC environments. The classification scheme is motivated, presented in detail with support from existing literature, and illustrated through a comparison of two types of weblog (blog) data. In concluding, the advantages and limitations of the scheme are weighed. Introduction It is by now a truism that computer-mediated communication (CMC) – defined here as predominantly text-based human-human interaction mediated by networked computers or mobile telephony – provides an abundance of data on human behavior and language use. Confronted with such abundance, researchers and practitioners have naturally sought to group, label, or otherwise organize CMC into categories that would facilitate its analysis and uses. However, there has been neither systematic discussion of how this should be done nor consensus regarding individual attempts to do so, many of which have been implicit and ad hoc. As a consequence, how to classify CMC remains a significant unaddressed problem of information organization. This article is concerned with the classification of CMC for research purposes, with a focus on online language and language use, hereafter referred to as computer-mediated discourse (CMD; Herring 1996, 2001). Specifically, it proposes an approach to the classification of CMD based on multiple categories or " facets " , a concept borrowed from classification theory in the field of library and information science. In contrast to applications in that field, however, which are primarily concerned with information storage and retrieval, the goal of the CMD scheme is to articulate aspects of context – both technical and social – that potentially influence discourse usage in CMC environments, and thereby to bring them to the conscious attention of the researcher. In this, it is akin in spirit to Hymes' (1974) etic grid, also known as the SPEAKING mnemonic, which is treated here as an early example of faceted classification in a research context. The organization of this article reflects its goal to motivate, articulate, and illustrate a model. The next section identifies the basic problem that gave rise to the need for a CMD classification scheme. Following a review of research on discourse classification, I then present an overview of the proposed faceted classification scheme for CMD and describe its dimensions and categories. This is followed by an illustration in which the scheme is applied to characterize contrasting computer-mediated (weblog) data samples. In concluding, the advantages and limitations of the faceted classification approach to online communication are weighed. The Problem Various attempts have been made by linguists to classify CMD, starting in the 1980s and early 1990s. Accustomed to dealing with two basic modalities of language – speech and writing –

We manually analyzed a corpus of Tumblr posts for sentiment, looking at images, text, and their combination. A dataset was constructed of posts with both text and images, as well as a dataset of posts containing only text, along with a codebook for classifying and counting the content in each. This paper reports on the construction of the overall corpus and the codebook, and presents the results of a preliminary analysis that focuses on emotion. Posts containing images expressed more emotion, more intense emotion, and were more positive in valence than posts containing only text. The study contributes a micro-level analysis of multimodal communication in a social media platform, as well as a gold standard corpus that can be used to train learning algorithms to identify sentiment in multimodal Tumblr data.

Research is scarce on how direct and indirect support seeking strategies affect support exchange in online health communities. Moreover, prior research has relied mostly on content analysis of forum posts at the post level. In order to generate a more fine-grained analysis of support exchange, we conducted content analysis at the utterance level, taking directness of support seeking, quality of provision, forum type, and seeker gender into account. Our analysis of four popular online support forums for people living with human immunodeficiency virus found that type of support sought and provided, support seeking strategy, and quality of emotional support provision differed in care provider/formal forums versus social/informal forums. Interestingly, indirect support seeking tended to elicit more supportive emotional responses than direct support seeking strategies in all forums; we account for this in terms of type of support sought. Practical implications for online support communities are discussed.