Filtered Food and Nofilter Landscapes: Role of Content and Visual Effects in Photo Engagement


Millions of images are shared through social media every day. Yet,
we know little about how the activities and preferences of users are
dependent on the content of these images. In this paper, we seek to
understand viewers engagement with photos. We design a quantitative
study to expand previous research on in-app visual effects (also
known as filters) through the examination of visual content
identified through computer vision. This study is based on analysis
of 4.9M Flickr images and is organized around three important
engagement factors, likes, comments and favorites. We find that
filtered photos are not equally engaging across different categories
of content. Photos of food and people attract more engagement when
filters are used, while photos of natural scenes and photos taken at
night are more engaging when left unfiltered. In addition to
contributing to the research around social media engagement and
photography practices, our findings offer several design
implications for mobile photo sharing platforms.