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Many social media portals are featuring annotation functionality in order to integrate the end users' knowledge with existing digital curation processes. This facilitates extending existing metadata about digital resources. However, due to various levels of annotators' expertise, the quality of annotations can vary from excellent to vague. The evaluation and moderation of annotations (be they troll, vague, or helpful) have not been sufficiently analyzed automatically. Available approaches mostly attempt to solve the problem by using distributed moderation systems, which are influenced by factors affecting accuracy (such as imbalance voting). Despite this, we hypothesize that analyzing and exploiting both content and context dimensions of annotations may assist the automatic moderation process. In this research, we focus on leveraging the context and content features of social web annotations for semi-automatic semantic moderation. This paper describes the vision of our research, proposes an approach for semi-automatic semantic moderation, introduces an ongoing effort from which we collect data that can serve as a basis for evaluating our assumption, and report on lessons learned so far.