This study aims at retrieving tweets with an implicit topic, which cannot be identified by the current query-matching system employed by Twitter. Such tweets are relevant to a given query but do not explicitly contain the term. When these tweets are combined with a relevant tweet containing the overt keyword, the "serialized" tweets can be integrated into the same discourse context. To this end, features like reply relation, authorship, temporal proximity, continuation markers, and discourse markers were used to build models for detecting serialization. According to our experiments, each one of the suggested serializing methods achieves higher means of average precision rates than baselines such as the query matching model and the tf-idf weighting model, which indicates that considering an individual tweet within a discourse context is helpful in judging its relevance to a given topic.
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