Fashion Conversation Data on Instagram

TL;DR

This paper analyzes fashion images on Instagram to understand what visual features attract engagement, using both manual tagging and neural networks, and provides a dataset for future research.

Contribution

It introduces a novel dataset of 24,752 labeled fashion images with visual and textual cues and offers insights into visual features that drive audience engagement.

Findings

Body snaps and face images attract more likes and comments.

Product-only images are most common but less engaging.

Neural network analysis aids in classifying influential fashion images.

Abstract

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.