The very rapid growth in user-generated social multimedia content on online platforms is creating new challenges for search technologies. A significant issue for search of this type of content is its highly variable form and quality. This is compounded by the standard information retrieval (IR) problem of mismatch between search queries and target items. Query Expansion (QE) has been shown to be an effect technique to improve IR effectiveness for multiple search tasks. In QE, words from a number of relevant or assumed relevant top ranked documents from an initial search are added to the initial search query to enrich it before carrying out a further search operation. In this work, we investigate the application of QE methods for searching social multimedia content. In particular we focus on social multimedia content where the information is primarily in the audio stream. To address the challenge of content variability, we introduce three speech segment-based methods for QE using: Semantic segmentation, Discourse segmentation and Window-Based. Our experimental investigation illustrates the superiority of these segment-based methods in comparison to a standard full document QE method for a version of the MediaEval 2012 Search task newly extended as an adhoc search task.