A Crowdsourced Data Set of Edited Images Online

Valentina Conotter, Duc-Tien Dang-Nguyen, Michael Riegler, Guilia Boato, Martha Larson
2014 Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia - CrowdMM '14  
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 used
more » ... as been used online. Such pairs are hard to find, and could not be collected, due to techno-practical constraints, without the support of crowdsourcing. This paper describes a three-step approach to data set creation involving mining social data, applying image analysis techniques, and, finally, making use of the crowd to complete the necessary information. We close with a discussion of the potential and limitations of the data set collected.
doi:10.1145/2660114.2660120 dblp:conf/mm/ConotterDRBL14 fatcat:h6ylypatcvhtnfd2tyvalyuz3u