Social media monitoring: What can marketers learn from Facebook brand photos?

Abstract

Users upload >350 million photos per day to Facebook. While considerable research has explored text-based user-generated content on social media, research on photos is still in its early stages. This paper uses a sample of 44,765 Facebook photos from 503 Facebook users in the United States and Germany to determine the degree to which photos play an integral role in people's social media communications. The analysis shows that uploading brand photos (i.e., photos containing a brand name or logo) is related to brand love, brand loyalty, and word-of-mouth (WOM) endorsement of the brand in question. We then code a subsample of these photos for content and train a powerful hybrid machine learning algorithm combining genetic search and artificial neural networks. The resulting algorithm is able to predict users' brand love, brand loyalty, and WOM endorsement from the content of their brand photos posted on Facebook. Finally, we discuss the implications for social media marketing, in particular social media monitoring.