Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Overtime, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task. In this paper, we address this challenge by identifying the information types presented in comments of OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy diverse needs of OSS stakeholders.