Connected and consuming: applying a deep learning algorithm to quantify alcoholic beverage prevalence in user-generated instagram images

Determining the prevalence of alcohol-related content on social media is important to guide education initiatives and interventions in this space. We aimed to assess the performance of the pre-developed alcoholic beverage identification deep learning algorithm (ABDILA) to automatically quantify alcoholic beverage prevalence in user-generated Instagram images. 6,121 images were gathered from Instagram using ‘Splendour in the Grass’ related hashtags, an Australian music festival. These images were manually annotated as containing beer, champagne, wine, or anything else. The images were subsequently run through ABIDLA, which made predictions on their same categorical contents. We then assessed overall model accuracy (relative to human annotations), model accuracy of alcohol-containing images (overall accuracy and across beverage categories), and visually inspected images to extract common features of congruent- or mis-categorisations. While overall accuracy was high, congruent classifications were heavily skewed towards non-alcohol images. The algorithm consistently overestimated the number of images containing alcoholic beverages, and inspection revealed that these false positives were largely driven by image context and colour. While such algorithms show early promise as a rough automated estimation tools for large datasets on social media, this study highlights some critical improvements and directions for applying pre-trained algorithms in this space.