The usage of social media in the context of the museum visit continues to grow. This research examined Instagram and Snapchat stories shared by visitors at the Brooklyn Museum via semi-structured interviews and photo-elicitation. The results provide insights into the characteristics of this ephemeral media and the motivations behind these posts. Similar to traditional photography, ephemeral content on social media is often motivated by capturing an artwork found to be aesthetically pleasing, documenting a feeling, sharing an experience, or building self-identity. However, the content shared is shaped by the ephemeral aspect that motivates minimal curation and editing. The study results add to the rapidly evolving field of social media within the museum context. Moreover, it advocates for an active role for the museum to have policies and opportunities that respond to these behaviors and learn from the content shared informing interpretation and learning materials.
Museums have deployed various research methods to evaluate the impact of their digital initiatives and better identify their users. This article discusses the methods and tools applied to the digital domain from a theoretical and practical point of view, using examples from the museum sector that illustrate the application of digital analytics to web sites, social media and mobile devices.
Computer vision algorithms are increasingly being applied to museum collections to identify patterns, colors, and subjects by generating tags for each object image. There are multiple off-the-shelf systems that offer an accessible and rapid way to undertake this process. Based on the highlights of the Metropolitan Museum of Art's collection, this article examines the similarities and differences between the tags generated by three well-known computer vision systems (Google Cloud Vision, Amazon Rekognition, and IBM Watson). The results provide insights into the characteristics of these taxonomies in terms of the volume of tags generated for each object, their diversity, typology, and accuracy. In consequence, this article discusses the need for museums to define their own subject tagging strategy and selection criteria of computer vision tools based on their type of collection and tags needed to complement their metadata.
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