My Own Private Nightlife

Abstract

Private nightlife environments of young people are likely characterized by their physical attributes, particular ambiance, and activities, but relatively little is known about it from social media studies. For instance, recent work has documented ambiance and physical characteristics of homes using pictures from Airbnb, but questions remain on whether this kind of curated data reliably represents everyday life situations. To describe the physical and ambiance features of homes of youth using manual annotations and machine-extracted features, we used a unique dataset of 301 crowdsourced videos of home environments recorded in-situ by young people on weekend nights. Agreement among five independent annotators was high for most studied variables. Results of the annotation task revealed various patterns of youth home spaces, such as the type of room attended (e.g., living room and bedroom), the number and gender of friends present, and the type of ongoing activities (e.g., watching TV alone; or drinking, chatting and eating in the presence of others.) Then, object and scene visual features of places, extracted via deep learning, were found to correlate with ambiances, while sound features did not. Finally, the results of a regression task for inferring ambiances from those features showed that six of the ambiance categories can be inferred with R 2 in the [0.21, 0.69] range. Our work is novel with regard to the type of data (crowdsourced videos of real homes of young people) and the analytical design (combined use of manual annotation and deep learning to identify relevant cues), and contributes to the understanding of home environments represented through digital media.