Towards automatic content analysis of social presence in transcripts of online discussions

This paper presents an approach to automatic labeling of the content of messages in online discussion according to the categories of social presence. To achieve this goal, the proposed approach is based on a combination of traditional text mining features and word counts extracted with the use of established linguistic frameworks (i.e., LIWC and Coh-metrix). The best performing classifier obtained 0.95 and 0.88 for accuracy and Cohen's kappa, respectively. This paper also provides some theoretical insights into the nature of social presence by looking at the classification features that were most relevant for distinguishing between the different categories. Finally, this study adopted epistemic network analysis to investigate the structural construct validity of the automatic classification approach. Namely, the analysis showed that the epistemic networks produced based on messages manually and automatically coded produced nearly identical results. This finding thus produced evidence of the structural validity of the automatic approach.