In particular grouping by aesthetics and quality of the media has brought along new challenges for Computational Aesthetics research such as what makes an image beautiful, what means beautiful and how do you quantify beautiful. to meet those challenges, researchers have tried to come up with several algorithms based in different metrics to bridge the gap between the quantitative aspects of what is called beauty and what people call beauty. In order to fill part of this gap we studied the effect of digital filters in photographic aesthetics so widely used in the social networks nowadays. Taking in consideration the popularity of digital filters among many social network users, it was a surprise to understand that most participants in the experiment preferred the images with no filter. In any case measuring what is beautiful always requires collecting aesthetics scores from people. Doing that collection process in a laboratory environment is the most effective approach. The main reasons are the highly controlled environment that leads to good data quality. The downside is cost, time and restriction of participants to the people available nearby. Therefore another issue addressed in the study was the use of crowdsourcing to minimize time and cost, as well as to expand the scope of participation, in the process of collecting image scores from users. To test that possibility a 4 step process step was designed and implemented. First preference scores were collected in a lab environment over a previous selected dataset. Afterwards the crowdsourcing experiment was planned what included an optimization of the dataset (ground truth dataset). Subsequently three digital filters were then applied to the collection and an online experiment followed to once again collect preference scores. In phase, we developed the experiment in the context of Microworkers and as a Facebook app interface enriched with a playful visual interface. The last step included a process to filter the suspicious participants and check results consistency. The results show that implementing an experiment to collect preferences of image quality in social media is a good methodology for Computational Aesthetics, if appropriate planning and management is adopted.