Human content perception has been underlined to be important in multimedia quality evaluation. Recently aesthetic considerations have been subject of research in this field. First attempts in aesthetics took into account perceived low-level features, especially taken from photography theory. However they demonstrated to be insufficient to characterize human content perception. More recently image psychology started to be considered as higher cognitive feature impacting user perception. In this paper we follow this idea introducing social cognitive elements. Our experiments focus on the influence of different versions of portrait pictures in context where they are showed aside some completely unrelated informations; this can happen for example in social networks interactions between users, where profile pictures are present aside almost every user action. In particular, we tested this impact on resumes between professional portrait and self shot pictures. Moreover, as we run tests in crowdsourcing, we will discuss the use of this methodology for these tests. Our final aim is to analyse social biases' impact on multimedia aesthetics evaluation and how this bias influences messages that go along with pictures, as in public online platforms and social networks.

International audienceMultimedia quality assessment has been an important research topic during the last decades. The original focus on artifact visibility has been extended during the years to aspects as image aesthetics, interestingness and memorability. More recently, Fedorovskaya proposed the concept of 'image psychology': this concept focuses on additional quality dimensions related to human content processing. While these additional dimensions are very valuable in understanding preferences, it is very hard to define, isolate and measure their effect on quality. In this paper we continue our research on face pictures investigating which image factors influence context perception. We collected perceived fit of a set of images to various content categories. These categories were selected based on current typologies in social networks. Logistic regression was adopted to model category fit based on images features. In this model we used both low level and high level features, the latter focusing on complex features related to image content. In order to extract these high level features, we relied on crowdsourcing, since computer vision algorithms are not yet sufficiently accurate for the features we needed. Our results underline the importance of some high level content features, e.g. the dress of the portrayed person and scene setting, in categorizing image