The ability to analyze the adequacy of supporting information is necessary for determining the strength of an argument. 1 This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning. Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NON-EXPERIENTIAL, or VERIFIABLE EXPE-RIENTIAL 2 , where the appropriate type of support is reason, evidence, and optional evidence,

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... evidence, respectively 3 . Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported. We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F 1 of 68.99%.