Instagram Images and Videos Popularity Prediction: a Deep Learning-Based Approach

In recent years, social media platforms have seen tremendous growth in terms of the number of users, forms of interaction, and diversity of content. While these channels are purely a source of entertainment for many users, for others they represent the main source of revenue or advertising for their products and services. In order to capture users’ attention, companies and professionals aim at achieving high popularity of their posts. In this work, we aspire to predict post popularity on the Instagram platform through Machine Learning approaches, with the goal of presenting a methodological tool that could provide useful information for post performance optimization. While previous contributions on the subject addressed the generic popularity of a post on the platform, we focus on the post popularity on a specific profile using only the visual content related to the post (image or video). We describe in detail the process and workflow to design a measure of popularity consistent even over the long time frame. Furthermore, we take advantage of state-of-the-art Convolutional Neural Networks and provide interpretability traits for their predictions, a quality that is nowadays highly welcomed in the industry. Lastly, we use a situation of scarce video data to experiment with ways of performing mixed training with both images and videos, providing problem-independent ideas and architectures that can potentially be applied to other video classification tasks.