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We present a crowdsourcing approach to tackle the challenge of collecting hard-to-find data. Our immediate need for the data arises because we are studying edited images in context online, and the way that this use impacts users' perceptions. Study of this topic cannot advance without a large, diverse data set of image/context pairs. The image in the pair should be suspected of having been edited, and the context is the place (e.g., website or social media post) in which it has been useddoi:10.1145/2660114.2660120 dblp:conf/mm/ConotterDRBL14 fatcat:h6ylypatcvhtnfd2tyvalyuz3u