This study aims to shed some light on the role of evaluative language in the process of persuasion in the newly emerging genre of 'online debate'. Drawing on the appraisal framework within Systemic Functional Linguistics, this study investigates the distributional patterns of appraisal choices and co-choices in a corpus of widely viewed online debate texts (ODTs). Based on the voting results of each ODT, textual parts of the corpus were segmented into two main categories: 'more persuasive' and

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... less persuasive' debaters. Supported by two specially designed software tools, the ODT corpus was manually annotated for appraisal features, and frequencies of choices and co-choices were extracted automatically. In line with previous research, the findings of this study revealed significant appraisal patterns associated with the ODT debaters, in addition to unique co-patterns characteristic of the 'more persuasive' and 'less persuasive' debaters. These findings are discussed in terms of potential implications, limitations and directions for future research.