Abstract
Snapchat is a social media application that allows users to take and send photos. It boasts 58.6 million users and has become one of the top used applications for millennials. Snapchat takes advantage of facial recognition algorithms to create and add filters to photos. Filters in this context are defined as digital additions or manipulations to photos, such as changing images to black and white or adding a rainbow in the foreground. In the following study, we studied how the choice of a snapchat filter influences perception of expression on that face. Subjects viewed face stimuli for 500 milliseconds and were asked to identify the expression. There were three possible filter states: fire filter, rainbow filter, or no filter. The expected valence of these filters was crossed with three types of expression: anger, happiness, and neutral. We compared whether different filters affected the perceived expressions in congruent (filter and expression matched) and incongruent (filter and expression mismatched) images. We found the number of correct responses to be significantly higher in congruent (versus incongruent) conditions, t(16) = 4.59, p < .001, and this was driven primarily by greater errors for angry expressions in the context of the rainbow filter. When the expression was happy we saw no effect of filter; however when the expression was angry we found the fire filter increased the likeliness people would rate the face as angry. For neutral expressions we found the presence of an active filter reduced accuracy, but not necessarily toward the filter. We observed no significant difference in reaction times. This study suggests filters can affect how we perceive expressions, which implies that these modified images are being perceived differently than original photos. More research about the contextual elements of expression needs to be done in the context of digital media.
Meeting abstract presented at VSS 2017