Inferring Artist Similarity From Social Media Content: The Instagram Case

In the last decade, digital technology and online platforms have revolutionized the music industry. Streaming services and social media have allowed artists to reach global audiences and curate personal, interactive relationships with fans. Traditionally, artist similarity has been assessed through audio analysis, but the industry shift necessitates new methods: social media offer a rich multimodal dataset for analyzing artist similarity, considering follower intersections, communication styles, and content types. This study investigates how Instagram behavior and content correlate with artists’ musical production. An early fusion approach combines visual and textual analysis via vision transformers and Bidirectional Encoder Representations from Transformers models, utilizing a Siamese neural network trained with triplet loss and cosine distance, in order to yield a high-dimensional representation of artist positions in an embedding space. Evaluation through accuracy, precision, and recall confirms that leveraging social media data to assess artist similarity leads to results comparable to those achieved by traditional audio-based models, thus highlighting the potential of social media analysis in understanding commonalities and differences among artists.