Laughter is an intrinsic component of human-human interaction, and current automatic speech understanding paradigms stand to gain significantly from its detection and modeling. In the current work, we produce a manual segmentation of laughter in a large corpus of interactive multi-party seminars, which promises to be a valuable resource for acoustic modeling purposes. More importantly, we quantify the occurrence of laughter in this new domain, and contrast our observations with findings for laughter in multi-party meetings. Our analyses show that, with respect to the majority of measures we explore, the occurrence of laughter in both domains is quite similar.